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Cost Efficient Provisioning of

Wireless Access

Infrastructure Cost Modeling andMulti-Operator Resource Sharing

KLAS JOHANSSON

Licentiate Thesis in TelecommunicationStockholm, Sweden 2005

Cost Efficient Provisioning of Wireless Access

Infrastructure Cost Modeling and Multi-Operator Resource Sharing

KLAS JOHANSSON

Licentiate Thesis in Telecommunication

Stockholm, Sweden 2005

TRITA–S3–RST–0520ISSN 1400–9137ISRN KTH/RST/R--05/20--SE

KTH Signaler, sensorer och systemSE-100 44 Stockholm

SWEDEN

Akademisk avhandling som med tillstand av Kungl Tekniska hogskolan framlag-ges till offentlig granskning for avlaggande av teknologie licentiatexamen freda-gen den 16 december 2005 klockan 14:00 i sal C1, Electrum, Kungliga TekniskaHogskolan, Isafjordsgatan 22, Kista.

c© Klas Johansson, december 2005

Tryck: Universitetsservice US-AB

Abstract

A cost efficient design of radio access networks is crucial for a continued growthof mobile data services. In this thesis we study two opportunities that operatorshave to lower their infrastructure costs; adapting network deployment to localvariations in traffic demand and multi-operator resource sharing.

With an increasing number of radio access technologies available, finding theproper mix of systems is a critical issue for the operators. For this purpose,we propose a model for estimating the total infrastructure cost as a functionof average traffic density. The main input parameters are the average cost,throughput, and range per base station. Based on a log-normally distributed,spatially correlated, traffic density map the network is dimensioned for a given setof base station types. With this network cost model, we illustrate how the costdepends on average traffic density for different single and multi-access networkconcepts. Moreover, we identify how the respective subsystem in a multi-accessnetwork should be improved in order to most effectively cut network costs.

Mobile infrastructure costs can also be reduced through network sharingbetween multiple operators, and this has lately been put in focus during thedeployment of the third generation’s mobile systems. With a joint radio accessnetwork, problems may arise in terms of free-rider effects, and there is a risk forconsciously misleading traffic forecasts with the objective to hide marketing plansfor competitors. This motivates a fair radio resource allocation between sharingoperators, in particular for cellular systems where over-dimensioning is quiteexpensive. To avoid a reservation of radio resources, which decreases averagecapacity utilization, we propose a load based priority queuing as an alternativesolution to this problem. Even without preemption of connected users, we showthat blocking levels can be sustained for operators with less than agreed load.This comes, however, at the cost of increased call setup times during congestion.

Furthermore, roaming between overlapping mobile networks could be ex-ploited to increase user data rates, in particular at the cell border. By means ofsimulations with three similar cellular overlaid networks, we quantify the gainwith national roaming for an urban scenario. The gains are, thanks to increaseddiversity against shadow fading, significant already with almost co-located basestations.

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Acknowledgements

Half way through the doctoral studies, I would like to take this opportunity toexpress my gratitude towards a number of persons who have contributed to thiswork. To start with, the collaboration with Anders Furuskar, Johan Hultell, andmy supervisor Professor Jens Zander, as well as the previous work with MartinKristensson at Nokia Networks has been very rewarding. I could not wish for amore professional and creative atmosphere!

Furthermore, the feedback from, and interesting discussions with, MagnusAlmgren, Bo Karlson, Peter Karlsson, Perttu Laakso, Jonas Lind, ProfessorGerald Maguire, Jan Markendahl, Guest Professor Osten Makitalo, Mats Nilson,Professor Bertil Thorngren, and Jan Werding have been very valuable. A specialthanks goes to Christian Bergljung for taking on the role as external reviewer atthe licentiate seminar, Martin who reviewed the licentiate thesis proposal, andto Anders, Bo, Johan, Martin, and Osten for commenting on the thesis draft.

The daily work with courses, teaching, etc., would probably not be endurablewithout the “class mates” within the Graduate School of Telecommunicationsand the colleagues at the Radio Communication Systems Lab (Pietro Lungaroand Bogdan Timus, to just mention a few). The administrative support fromNiklas Olsson, Irina Radulescu, and Lise-Lotte Wahlberg is also very much ap-preciated.

I would also like to thank all my friends and former colleagues who, in oneway or another, inspired me to initiate this education and cheer along the way ofthis mental marathon. In particular, David Astely, Christian Braun, Ann-LouiseJohansson, Mats Larsson, Professor Preben Mogensen, and Klaus Pedersen fromthe research department of Nokia Networks, who taught me the engineering ba-sics and for sharing their enthusiasm for radio communications. I also very muchappreciate all activities with friends from the Electrical Engineering program atKTH, skiing, and radio broadcasting, which to an equally large extent contributeto my personal development.

Above all, though, I am very grateful and happy to have my beloved Maria,my parents Britta and Lars, and my sister Jenny. Their love and support istruly admirable.

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Contents

I 1

1 Introduction 3

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Scope of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.3 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2 Infrastructure Cost Modeling 13

2.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.3 Delimitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.4 Base Station Characteristics (Paper 1) . . . . . . . . . . . . . . . 21

2.5 Heterogeneous Infrastructure Cost (Paper 2) . . . . . . . . . . . 23

2.6 Cost Efficient Capacity Expansion (Paper 3) . . . . . . . . . . . 28

2.7 User Deployed Access Points (Paper 4) . . . . . . . . . . . . . . . 33

2.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3 Multi-Operator Resource Sharing 39

3.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.3 Fair Resource Sharing (Paper 5) . . . . . . . . . . . . . . . . . . 43

3.4 Throughput with National Roaming (Paper 6) . . . . . . . . . . 46

3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

4 Concluding Remarks 55

4.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

4.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

References 59

Appendices 67

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viii Contents

A Cost Modeling and Pricing Strategies 69A.1 Discounted Cash Flow Modeling . . . . . . . . . . . . . . . . . . 69A.2 Pricing Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

B Network Sharing Use Cases 73B.1 Mobile Virtual Network Operators . . . . . . . . . . . . . . . . . 74B.2 Inter-Operator Charging with Roaming Based Sharing . . . . . . 75

II Paper Reprints 77

5 Base Station Characteristics (Paper 1) 79

6 Heterogeneous Infrastructure Cost (Paper 2) 87

7 Cost Efficient Capacity Expansion (Paper 3) 95

8 User Deployed Access Points (Paper 4) 103

9 Fair Resource Sharing (Paper 5) 109

10 Throughput with National Roaming (Paper 6) 117

List of Tables

2.1 Single carrier WCDMA BS performance assumptions . . . . . . . 232.2 Cost and performance estimates for different technologies . . . . 272.3 Summary of base station densities and infrastructure costs . . . . 30

3.1 System parameters used in national roaming simulations . . . . . 50

ix

List of Figures

2.1 Approximative macro cell ranges at different carrier frequencies . 142.2 Cost structure of cellular BSs . . . . . . . . . . . . . . . . . . . . 242.3 Cost per area unit covered with WCDMA (uniform traffic) . . . 242.4 Example of a generated traffic map and network deployment . . 262.5 Cost for different combinations of radio access technologies . . . 292.6 Cost for a multi-access network with different macro cell radii . . 312.7 Elasticity of infrastructure cost in a multi-access network . . . . 322.8 Fraction of traffic covered with user deployed APs . . . . . . . . 342.9 Cost for various mixes of user and operator deployed APs . . . . 35

3.1 Principle network sharing methods . . . . . . . . . . . . . . . . . 443.2 Admission control with non-preemptive priority queuing . . . . . 463.3 Probability of blocked calls per operator . . . . . . . . . . . . . . 473.4 An illustration of the inter-operator site distance model . . . . . 493.5 Expected gain in user throughput gain with national roaming . . 52

xi

List of Abbreviations

1G, . . . , 4G First, . . . , Fourth Generation of Mobile Systems3GPP Third Generation Partnership ProgramAP Access PointARPU Average Revenue Per UserBS Base StationCAPEX Capital ExpendituresCDMA Code Division Multiple AccessDCH Dedicated ChannelDSL Digital Subscriber LineGSM Global System for Mobile CommunicationsHSDPA High Speed Downlink Packet AccessIEEE Institute of Electrical and Electronics EngineersIEEE 802.11a/b/g WLAN standards authorized by IEEEMNO Mobile Network OperatorMVNO Mobile Virtual Network OperatorO&M Operation and MaintenanceOPEX Operational ExpendituresQoS Quality of ServiceRAN Radio Access NetworkRRM Radio Resource ManagementS3G Super 3G (Long Term Evolution of 3G)SIR Signal to Interference (plus noise) RatioSLA Service Level AgreementUMTS Universal Mobile Telephony SystemWCDMA Wideband Code Division Multiple AccessWLAN Wireless Local Area Network

xiii

Part I

1

Chapter 1

Introduction

In this thesis we treat two topics of relevance for a cost efficient capacity expan-sion of mobile data networks. More specifically:

1. How mobile network operators (MNO) could exploit various radio accesstechnologies in order to cut infrastructure costs.

2. The specific technical problems and possibilities that arise when multipleoperators (service providers) share the same radio access network (RAN).

Next we will outline the background to the study and a few underlying assump-tions, and thereafter define and motivate the general problem addressed andthe thesis scope. More detailed problem descriptions and a review of previousrelated work are included in Chapter 2 and 3 respectively, which also summarizethe included papers.

1.1 Background

Evolution of the Mobile Internet

The first generation of mobile systems (1G) was launched in the beginning ofthe 1980s. A decade later, service offerings were exclusively targeted towardsbusiness users and the service penetration rate was low. However, after the in-troduction of second generation systems (2G), prices declined during the secondhalf of the 1990s and mobile telephony was surprisingly soon adopted by mostpeople in the developed countries [1].

At the same time, the Internet, with services like web-browsing, file down-loads, and e-mail, changed people’s way of living and doing business. The successof the Internet was enabled by

• Moore’s law, resulting in continuously increasing computer processing andmemory capabilities,

3

4 Chapter 1. Introduction

• the fact that packet switched transmission in principle allow users to alwaysbe connected without allocating expensive network resources, and

• an open network architecture which basically allowed anyone to provideinteresting services and applications.

Today the majority of the population in industrialized countries is connectedand penetration rates for residential access have more than doubled since themillennium shift [2, 3].

In the mid 1990s, the business cases of mobile telephony and the Internetmerged into a common vision – “the Mobile Internet”. The business logic seemedobvious; mobile users could access a tremendous amount of useful and entertain-ing Internet based services wherever they where. This would clearly open up fornew revenue streams and the Mobile Internet was incredibly hyped. However,while predictions at that time suggested that the average data traffic volumeswould reach 150MB per user in January 2004 [4], voice services are still prevailingin most countries.

Demand for Wireless Access

The demand for telecommunications in general, and mobile telephony in partic-ular, is largely driven by the value of

• having the opportunity to communicate, and

• network effects1 [6].

To a large extent, these factors explain the demand for coverage and interna-tional roaming in mobile networks, as well as inter-connection of networks. Itfurthermore partly motivates the importance for telecom operators to have alarge subscriber base; having that, they can capture parts of the value thatnetwork effects brings [6].

Wireless Local Area Networks (WLAN), on the other hand, are today mainlyused for lap-tops and handhelds tailored for data services and the user behavioris essentially the same as with fixed broadband, or local area networks. Thatis, Internet-access, file transfers, etc., at homes and in offices which benefit froma short transmission delay (per message, file, etc.). We can thus observe thatwhile area coverage and mobility have been important for telecommunicationservices, a high data rate is often valued more for data oriented services.

The Cost of Coverage and Mobility

In spite of the increasing number of radio access technologies with various charac-teristics available [7], systems that are capable of providing Internet connectivity

1By network effects, we include both that a network is worth more if more services andpeople are accessible (Metcalfe’s law [5]), but also that an established communication per sewill lead to more communication.

1.1. Background 5

with wide area coverage and support for mobile usage are still quite expensive.Mobile telephony, on the other hand, has evidently been a good stroke of busi-ness and the demand for coverage and mobility has driven the mobile systemstowards

• base stations (BS) with high output power and highly mounted antennas,

• sophisticated mechanisms for mobility management and fast handoversbetween cells, and

• complex billing systems supporting various pricing strategies (see furtherAppendix A.2).

As a consequence, entry barriers have been high in the wireless infrastructuremarket, and the cost of switching supplier is an important criteria when an MNOpurchase new infrastructure.

A key enabler for the success of mobile telephony has been the availability ofspectrum. Since voice and messaging services (personal communications) onlyrequire a low average and peak data rate per session, a relatively small bandwidthof radio spectrum is sufficient. Regulators have therefore, so far, been able to putspectrum in suitable frequency bands at the operators’ disposal.2 In conjunctionwith the low data rate requirements per link, the maximum feasible range perBS has also been quite high (in the order of 10–30km in 1G and 2G). Hence,relatively few BSs were required for (almost) full area coverage and today wehave nationwide coverage for mobile telephony in most developed countries.

The situation is quite different for services requiring higher peak and meandata rate per session. From the supply perspective, higher data rates implyboth higher bandwidth requirements, which have been difficult to find in lowerbands, and shorter feasible communication distances. This fact complicatesspectrum assignment procedures and requires denser networks (which might bequite expensive). Already in third generation systems (3G), targeted for datarates in the order of 100kbps with full area coverage, cell radii in the orderof a few hundred meters are required to obtain good indoor coverage in urbanenvironments. Hence, significantly higher data rates (that is, > 1Mbps) aremost likely economically feasible only in specific places, like malls and largeenterprizes, and not in general.

Spectrum Regulation

Spectrum allocation procedures have varied considerably between countries andservices, and is an intricate question from both a technical, societal, and business

2Both the carrier frequency and bandwidth strongly affect what combination of area cov-erage and data rate per link that can be served with a BS. Propagation path gain decrease asa function of carrier frequency and, according to the Shannon bound, (R = Wlog2(1 + SIR)),data rates R increase linearly with system bandwidth W , but only logarithmical with thesignal to interference ratio (SIR).


perspective [6,8]. Radio spectra have traditionally been reserved for specific ser-vices and technologies, and concessions are handed out (primarily) to broadcast-ing companies, military organizations, and telecom operators. This paradigm iscurrently being challenged, and with an increasing number of analogue radio sys-tems being replaced by digital successors, large portions of spectrum is at stakealso in lower frequency bands. These bands are very attractive for a plethora ofmobile services, including broadcasting, personal communications, and internetaccess.

Parts of the radio spectrum have also been allocated to low-power transmit-ters, which do not require a license (for example, the bands used for WLAN andother short range technologies). Despite that the range is poor at high frequen-cies, it is most often sufficient for indoor systems. Moreover, an advantage withhigh carrier frequency for indoor BSs is that the channel can be reused alreadyin adjacent buildings and floors, and there is, as oppose to for mobile systems,no particular need for exclusive spectrum use rights.

The high value of radio spectrum adequate for mobile services was evident inthe the recent 3G license auctions in Europe. To use auctions or not, and howto design the auction process, has been discussed for more than forty years [6,9].Typically only a few (3–5) MNOs are currently targeted per market to ensurethat reasonable quantities are available at affordable prices, while allowing oper-ators to cover their fixed costs [6,10]. However, to keep license fees at reasonablelevels (avoiding the “winner’s curse” phenomenon3), it has been stressed thatat least one more license than in previous systems has to be awarded when newsystems are introduced [6].

Since the spectrum allocation process becomes increasingly complex, espe-cially considering the multitude of systems and services available, alternativesolutions are currently being investigated. Instead of long term allocations,spectrum is envisaged to be assigned more dynamically for different services andtechnologies, and with finer granularity; see for example [12,13].

Inter-connection Regulation

Both in the copper-line access network and mobile networks, regulators try tostimulate competition by enforcing the owner of the access networks to allow forindependent (virtual) operators to resell access subscriptions. In particular forresidential broadband subscriptions, the legacy telephony networks have provento be sufficient for the majority of consumers, and the low-cost Digital SubscriberLines (DSL) have clearly boosted the residential broadband penetration in manycountries [3]. As demand takes off, DSL operators can then expand their businessgradually and eventually roll-out their own fiber networks instead of facing hugeinvestments upfront for the “last mile” access network.

3Meaning that the winner of an auction realizes that the object was more worth for himthan the other participants, and thus overbid [11].

1.2. Scope of the Thesis 7

Price regulation is frequently applied for inter-connection charges betweenoperators in order to avoid that licensees and incumbent fixed-line operators setinter-connection charges too far above the production cost [6, 14, 15]. There is,however, a risk that firms fail to recover “sunk costs”4 with cost based priceregulation whereby this needs needs to be done carefully.

1.2 Scope of the Thesis

General Problem Description

Mobile data services are widely believed to bring great opportunities for thesociety, just as the fixed Internet and mobile telephony already have. And, as amatter of fact, research and development towards future wireless infrastructureis today essentially driven by a desire to increase data rates in order to supporta greater variety of services [16, 17]. However, as outlined above, there are stillmany issues that need to be solved in order to attract the average consumer.

Besides innovation and marketing of new services that fulfill user needs, wecan presume that the cost of production needs be reduced significantly (espe-cially where user density is low). Whereas steadily increasing data rates, thanksto the diminishing cost of electronics, have been feasible to offer at a flat ratein fixed broadband networks,5 it is questionable if the same will hold true alsofor mobile systems. This since denser networks ultimately are required if sig-nificantly higher data rates are to be provided, which is associated with highcosts for site buildout, rental, etc. [18]. Alternatives to building yet denser cel-lular networks are therefore important, and that is the point of departure of thisthesis.

Research Approach

The cost of providing mobile data services can be reduced in several ways. Ifwe limit the scope to the RAN, which typically constitutes the bulk of theinfrastructure cost, an MNO could in principle choose to:

• Improve the physical layer transmission techniques.

• Exploit service requirements and propagation channel characteristics inradio resource management (RRM).

• Automate the network planning and optimization processes.

• Acquire more radio spectrum in sufficiently low frequency bands.

4Sunk costs have traditionally been high for telecom operators, both for mobile networksand fixed line telephony infrastructure.

5Once the cabling is in place, data rates in fixed networks can readily be increased byupgrading routers, switches, etc. That is, electronics, for which the cost diminishes accordingto Moore’s law.


• Adapt BS capabilities to local coverage and capacity requirements usingmulti-access networks or hierarchical cell structures.

• Form a network sharing agreement with other operators in areas wherethe own customer base is to small to cover the fixed costs.

Whereas the three first methods have been the traditional focus of technicalresearchers in radio communication systems, the other are rather related tonon-technical issues; such as traffic demand, regulations, competitive strategy,and financial considerations. Even though we acknowledge the importance ofimproving the technical performance of different systems and the value of ad-equate spectrum allocations, we will focus on the two latter methods. This inorder to investigate to what extent these solutions, which are in the borderlandbetween technology and business, are useful as a means to lower infrastructurecosts for mobile data networks.

Throughout the thesis the focus will be on low and moderately high datarates (up to approximately a few hundred kbps per user). This is motivated bythe reasoning presented in Section 1.1. That is, that only moderate data ratesare economically viable to provide with wide area coverage in mobile networks.6

Contributions

More specifically, we will analyze

• the infrastructure cost of wireless networks adapted to a geographicallyvarying traffic load, and

• multi-operator resource sharing.

Brief problem statements and a summary of the contributions within the respec-tive research topic are provided next.

Infrastructure Cost Modeling

Operators can choose to deploy specific cellular indoor solutions and WLAN toprovide coverage for high data rates in specific places. This typically requires ahigh willingness to pay per user, and is today most common in airports, hotels,etc. Traditionally, though, hierarchical cell structures have also been used ascapacity fill-in in zones with high traffic density. However, with WLAN tech-nology available, operators also have the option to deploy multi-access networksinstead of hierarchical cell structures if traffic demand increases significantly.

While it is widely accepted that future networks will consist of a blend ofradio access technologies [19–22], less is known about what cost savings that canbe expected with such heterogeneous wireless networks. The primary objective

6For some specific places and services, for instance fixed wireless access, higher data ratescould still be worthwhile to offer, but such deployments are outside the scope of this thesis.

1.2. Scope of the Thesis 9

herein is therefore to quantify to what extent an operator can lower its infrastruc-ture costs by utilizing BSs with different characteristics to cover a non-uniformspatial traffic density. In particular, we will compare the cost of using WLANaccess points in traffic hot spots with a conventional (single-access) hierarchicalcell structure. Moreover, the need for improved cellular systems and alternative,user deployed, expansion strategies will also be considered.

For this purpose an infrastructure cost model has been developed, which hasbeen used to evaluate different combinations of single and multi-access systemsin the following conference contributions (which are summarized in Chapter 2):

1. Klas Johansson, Anders Furuskar, Peter Karlsson, and Jens Zander, “Re-lation between base station characteristics and cost structure in cellularsystems”, In Proc. PIMRC 2004 [23].

2. Anders Furuskar, Klas Johansson, and Magnus Almgren, “An Infrastruc-ture Cost Evaluation of Single- and Multi-Access Networks with Hetero-geneous Traffic Density”, In Proc. VTC2005 Spring [24].

3. Klas Johansson and Anders Furuskar, “Cost efficient capacity expansionstrategies using multi-access networks”, In Proc. VTC2005 Spring [25].

4. Klas Johansson, “On the cost efficiency of user deployed access pointsintegrated in mobile networks”, In Proc. RVK 2005 [26].

The author of the thesis was main responsible for the first, third, and fourth pa-per. Anders Furuskar contributed with significant parts of the simulation modelsand was the primary author of the second paper. All modeling and the researchapproach has been developed jointly by the author and Anders Furuskar. Pe-ter Karlsson contributed with valuable comments and ideas in particular on thefirst paper. Magnus Almgren assisted with the initial research approach andnetwork dimensioning principles used in the three last papers. Jens Zander hasas advisor been involved and provided valuable feedback and guidance in allpapers.

Multi-Operator Resource Sharing

Network sharing has recently been put into practice by some 3G operators andthe first operational networks were recently deployed in Sweden [27–29].7 Tech-nically, multiple operators access the same RAN using, to a large extent, mech-anisms originally designed for international roaming. In a more general sense,roaming based network sharing also includes Mobile Virtual Network Operators(MVNO). National roaming between geographically overlapping cellular net-works could also be exploited by operators to reduce their risk exposure whenintroducing new services.

7Rudimentary forms of network sharing, such as site and antenna sharing were widely usedalso in 2G; see further Appendix B for a summary of network sharing methods.


The advantages with network sharing are promising, especially when con-sidering the new business possibilities for MVNOs (see further Appendix B.1)and that mobile data services could become economically viable also in less pop-ulated areas. However, there are considerable drawbacks in terms of reduceddifferentiation possibilities as well as administrative and technical overhead.Hence, the transaction costs may be significant, in particular for incumbentoperators [27–31], which will be discussed further in Section 3.1.

We have studied technical solutions to one specific problem that has beenraised; namely how network resources can be allocated in a fair way betweenoperators sharing a cellular network. We will also investigate to what extentcoverage for higher data rates can be increased by means of national roaming.This in order to further examine what the long-term use case of network sharingmight be for mobile network operators. These two problems were addressed inthe following two papers (summarized in Chapter 3):

5. Klas Johansson, Martin Kristensson, and Uwe Schwarz, “Radio ResourceManagement in Roaming Based Multi-Operator WCDMA networks”, InProc. VTC2004 Spring [32].

6. Johan Hultell and Klas Johansson, “An Estimation of the Achievable UserThroughput with National Roaming”, Submitted to VTC2006 Spring [33].

The author of the thesis was main contributor to the first paper, for whichMartin Kristensson, Uwe Schwarz, and also Preben Mogensen contributed withvaluable feedback, both on the initial ideas and concept, and in identifying usecases for fair radio resource sharing between operators. The second paper wasjoint work with Johan Hultell where both contributed to an equal extent in allaspects. Jens Zander provided feedback and guidance for both of the papers.

Other Related Papers

The following papers are related to, but not included, in the thesis and discussmulti-operator RRM respectively business models for user deployed access points(AP) at a conceptual level.

• Johan Hultell, Klas Johansson, and Jan Markendahl, “Business models andresource management for shared wireless networks”, In Proc. VTC2004Fall [31].

• Klas Johansson, Jan Markendahl, and Per Zetterberg, “Relaying accesspoints and related business models for low cost mobile systems”, In Proc.Austin Mobility Roundtable, 2004 [34].

• Klas Johansson, Jonas Lind, Miguel Berg, Johan Hultell, Niklas Kviselius,Jan Markendahl, and Mikael Prytz, “Integrating User Deployed Local Ac-cess Points in a Mobile Operator’s Network”, In Proc. WWRF meeting#12, 2004 [35].

1.3. Thesis Outline 11

1.3 Thesis Outline

The thesis consists of two parts. The first part contains a presentation of theresults and discussion of the included papers of the respective topic in Chapter 2and Chapter 3. The thesis is summarized and recommendations for future workare outlined in Chapter 4. In the second part, consisting of Chapter 5–10, theseries of papers that constitute the contributions of this thesis are reprintedin verbatim. We have also included a brief overview of commonly used costterminology, pricing strategies, and network sharing use cases in Appendix A–B.

Chapter 2

Infrastructure CostModeling

In this chapter, we will analyze to what extent the incremental cost associatedwith increasing traffic volumes can be reduced by adapting network capacityand base station capabilities to local variations in demand. More specifically, wewill comparemulti-access networks with conventional hierarchical cell structures.Whereas the former refers to systems where multiple radio access technologies(cellular and WLAN) are accessed with a multi-radio capable terminal, the lat-ter describes a single-access network constituted of various cell sizes (macro,micro, etc.). As compared to hierarchical cell structures, multi-access networksin essence have the benefit that other (simpler) protocol stacks and larger (un-licensed) spectrum bandwidths can be used in local access systems (WLAN),yielding cheaper systems and higher data rates. The advantage with hierarchi-cal cell structures, on the other hand, is that handsets and central systems onlyneed to support one access method.

It is well known that considerable cost savings can be obtained by adaptingradio access systems to the data rates, capacity, and degree of coverage required.For example, a system deployed in rural areas need a long range, but relativelyfew users are served per BS. Consequently, a smaller chunk of spectrum in lowerfrequency band is preferred as compared to a wide bandwidth at a higher fre-quency; see further Figure 2.1. Down-town areas with heavy traffic, on theother hand, seldom require long range so the requirements on technology are theopposite.1

If traffic is uniformly distributed within a service area, the network design is,from an engineering perspective, straightforward since one technology essentiallyminimizes cost for a given scenario. With a heterogeneous spatial traffic density,

1In suburbs and urban areas with low-rise buildings, which in many European countriescover the largest part of the population, BSs with medium range and capacity are typicallymost cost efficient.

13

14 Chapter 2. Infrastructure Cost Modeling

100 200 300 400 500 600 700 800 900 10000

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Distance from base station [m]

Upl

ink

user

thro

ughp

ut (o

utdo

ors)

[Mbp

s]1GHz2GHz3GHz

Figure 2.1: A rough estimation of user throughput as a function of cell range for afew carrier frequencies in a noise limited system. The COST231-Hata model foroutdoor, urban, propagation has been used (with parameters according to [25])and throughput is calculated using the Shannon-bound, with 3.84MHz carrierbandwidth. The figure illustrates how valuable spectrum in lower bands is forservices requiring area coverage.

though, introducing a mix of different BSs and radio access technologies withvarying characteristics could potentially reduce costs. In this chapter we willtherefore examine if there are any cost advantages to take local variations intraffic load into account when deploying a heterogeneous wireless network. Acase study with a few common radio access technologies will be performed. Thefocus in on infrastructure; even though terminals constitute a significant costfor mobile operators and users, we assume that especially data and multi-mediahandsets need to be multi-mode anyway to support legacy mobile systems andprivate WLANs.

2.1 Related Work

Previous work on the cost structure of wireless access networks has mainly beenconducted during the development of the Universal Mobile Telephone System(UMTS) [1, 10, 18]. National regulators have further on developed cost mod-els for regulation of inter-connection charges and spectrum assignment guide-lines [10,14,15,36,37]. Technology road-maps and visions, supported by econom-ical reasoning, have also been presented by telecom equipment vendors [4, 38],

2.1. Related Work 15

and technical researchers have to some extent used infrastructure cost as ob-jective function for optimization of network dimensioning [39–44]. All of thesestudies cover both useful methodology and empirical data on unit costs, and aresummarized next.

Business case feasibility studies

Infrastructure cost models have previously been applied in investment analysesfor wireless access provisioning. That is, the objective of these studies is to eval-uate whether or not introducing a new communication technology is profitable.Significant contributions have been made within the European Unions’ researchprograms RACE II (Research of Advanced Communication Technologies), ACTS(Advanced Communication Technologies and Services), and the recently finishedTONIC (Techno-Economics of IP Optimized Networks and Services) project. Inparticular, a methodology for techno-economical evaluation of access networksfor telecommunication services was developed during 1992-1996 in the TITANproject (Tool for Introduction Scenario and Techno-economic Evaluation of Ac-cess Network), as part of RACE II [45]. This was later on refined within ACTS,which finished in year 2000, under the acronym TERA (Techno-economic resultsfrom ACTS), and in TONIC [1].

While RACE II and ACTS subsequently led to the development of networkarchitectures and radio access technologies for UMTS, TONIC was mainly car-ried out during the downturn in the telecom sector, shortly after the millennium-shift. With the widely debated license fees and high investments for 3G net-works [6, 46], the aim was to quantify the long-term profitability for UMTSoperators. WLAN hot spots were also addressed, since it rapidly had emergedas a competing technology to UMTS. A number of typical scenarios were eval-uated, including small and large European countries and various operator sizes.The main results of TONIC were presented in terms of net present values (andinternal rate of return), and demand was modeled as a function of time using “S-curves” [1]. The pay-back time was estimated to be approximately seven years,which was not considered to be too long having in mind that the concessionstypically have a duration of 20 years. Clearly this fact will be a significant entrybarrier for alternative technologies that now try to enter the mobile market [7].

Following up on TONIC, the CELTIC initiative recently launched ECOSYS(techno-ECOnomics of integrated communication SYStems and services). Theaim with that project is to provide insights into risk management for new marketactors providing fixed and mobile Internet access and services, in an increasinglyheterogeneous market for wireless infrastructure. Among their early contribu-tions is an overview of demand forecasts for fixed and mobile networks andservices in Europe [3]. This includes an overview of the market share of dif-ferent broadband technologies, and their distinguishing characteristics, showingthat residential broadband penetration currently increase exponentially withinWestern Europe (with Sweden in the forefront).


Similar methodology has been used to analyze the profitability of SwedishUMTS licensees [46]. It was concluded that the profits of Swedish operatorswould decrease with UMTS, in principle due to larger investments and oper-ational expenditures (OPEX) at sustained average revenue per user (ARPU).This was, however, based on the operators’ initial license applications, whichpromised a significant UMTS coverage also in the rural parts of Sweden. At themoment, however, the Swedish regulator and operators are considering alterna-tive technologies in the 450MHz band (which still is used for 1G) to cover lesspopulated areas.

A case study of the economical feasibility of public WLAN deployed in a largescale in Stockholm was presented in [47]. It was shown that there is a potentialfor pure WLAN operators that provide limited coverage and mobility in publicplaces, such as museums, bus stops, inner city squares, etc. The users’ willingnessto pay has to exceed approximately 50SEK per month in order to cover the costsfor infrastructure. However, the author pointed out that revenues need to beconsiderably higher for the business case to be considered as profitable for aprivate actor due to the high risk. An option, proposed in [47], would instead bethat the public sector should deploy a public WLAN network in selected placesprovide access, possibly free of charge, until demand increase and private actorsfind it profitable to offer public WLAN access.

Network planning and optimization

Improving the link budget and the throughput per BS is as previously mentionedwidely accepted as an effective method to increase cost efficiency in mobile net-works. The financial benefits with a few capacity enhancement techniques wereanalyzed in [38, 39]. For instance in [38], a net present value analysis was pre-sented for the case of introducing advanced antenna concepts in 3G systems.However, no additional costs were associated with the capacity enhancing fea-tures.

The problem of optimizing BS range and aggregate throughput versus costwas treated in [40, 41, 48]. All these studies have the infrastructure cost as ob-jective function for network optimization. For example, in [41], an optimumpower allocation and placement of BSs with respect to infrastructure cost wasconsidered. Under the assumption of uniformly distributed traffic load and atarget blocking probability the optimization problem was shown be convex, andthus a global optimum can be found. The cost of a BS was modeled as a linearfunction of the composite output power and it was observed that minimizingthe number of BSs, which was done in for example [49,50], not necessarily mini-mize overall infrastructure costs considering the lower cost for smaller BS types.Moreover, the cost structure shift from BSs and physical infrastructure to ”lastmile”-transmission as BS size decreases and consequently, as noted in [41], low-ering cost for the actual BS itself does not necessarily lower total infrastructurecost.


In wireless networks the fixed part of the “last mile”-transmission standfor a significant portion of the overall infrastructure costs – in the order of5-15% [1, 44]. Optimization of the topology and aggregation points (hubs instar-shaped networks) has been quite extensively studied; see for example [1,41]and references therein.

Methods to lower BS site costs (in total) were also discussed in [42], whereit was noted that pedestrian users do not need as advanced BSs as vehicular do.Moreover, cost reductions of 60-70% per subscriber were estimated by introduc-ing a micro-cell layer in second generation mobile systems.

Automated solutions are also often sought in order to reduce both invest-ments and running costs by a proper cell planning and network management.An overview of the potential cost reduction with different automation solutionswas presented in [43]. Automatic network optimization techniques are popularsince they do not require additional hardware (or at least very little). However,it was also noted in [43] that an improved physical layer also is required if anorder of magnitude higher capacity is needed.

Residential broadband access

The aforementioned European research projects also covered techno-economicalfeasibility of fixed broadband networks. Already within RACE, the TITANproject developed methodology and tools for analyzing the net present valueand assess risks associated with providing broadband services [51].

It was argued in [51] that the most critical parameters to include in a techno-economical model for broadband systems are subscriber density, civil works con-figuration, component cost evolution, and demand assessment (service penetra-tion). The copper network was showed to be the cheapest solution. In ruralareas, however, wireless access (also called “radio in the local loop”) was signif-icantly cheaper than fixed line alternatives. Component prices were too high atthat time (1996) for fiber based solutions to be considered profitable. However,it was stressed that fiber based infrastructure will be increasingly interesting forfixed broadband access, in particular considering the expected lower operationand maintenance (O&M) costs and the potential to offer more services.

It is clear that the incremental cost for installing fiber is significantly higherthan DSL. The cost per fiber line (residence) was at the time of these studiesabout US$2000-2500, whereof approximately US$1500 is for the passive opti-cal network [52]. This should be compared to the incremental cost for a DSLconnection, which is less than a tenth of that [53].

Wireless fixed broadband systems have recently appeared in the market,especially for rural markets. Although it has been available for 5-10 years, awider interest for these solutions occurred only recently. The reason for this isprobably the growing broadband market as a whole, including digital subscriberline (DSL) systems, a political interest to make broadband available for everycitizen, and an increasing competition amongst radio access technologies and


operators. There are already a number of systems available, including the timedivision duplex mode of WCDMA, IEEE 802.16a (WiMAX) and early fourthgeneration (4G) system concepts. A techno-economical feasibility study of IEEE802.16a was presented in [53], based on the methodology and tools developedwithin ACTS and TONIC. The results showed that the cost structure of fixedwireless access can not compete with fixed DSL in cities and suburbs due tohigh equipment prices and low range of the systems, especially in the currentlyavailable 3.5GHz frequency band. This was also concluded in [7]. However, forrural areas and regions with inferior infrastructure the fixed broadband wirelessaccess systems look more promising and as compared to mobile systems the linkbudget is significantly better thanks to that directional roof-top antennas canbe used (as with terrestrial TV-broadcasting).

Assessments of Spectrum Allocations

Having access to radio spectrum is, as discussed in Chapter 1, indeed a funda-mental issue for MNOs. This topic has also been much debated during recentyears, and there are several contributions on the topic. The value of spectrumfor UMTS operators was assessed in a consultancy report commissioned by theSwedish regulator “Post och Telestyrelsen” in 2004 [36]. This study also in-cluded estimates of the infrastructure cost, in order to quantify the economicalbenefits of allocating additional spectrum for Swedish UMTS operators. It wasconcluded that the cost of BSs constitute a large part of the total cost of thenetworks, and that minimizing the number of BSs therefore is important. Formore densely populated areas, capital expenditures (CAPEX) could be reducedin the order of 2-6 billion SEK if more spectrum can be added instead of newsites (one BS costing approximately 1 million SEK). Savings in OPEX was as-sumed to be less significant, in the order of 5% of CAPEX. However, since theneed for additional capacity occurs in the future, the present value is lower (800MSEK). This study does not take technology advances into account and due tothe difficulty of making traffic forecasts it was concluded that the point of timewhen additional spectrum is needed is difficult to predict.

A thorough cost analysis was also presented in a report for the Federal Com-munications Commission in 1992, as part of a quite extensive study assessingthe spectrum required for “personal communication services” [10]2. A case studywas conducted for a hypothetical residential area of 25600 households which wasto be provided personal communication services. The focus was on potential eco-nomical synergies between telephony services, cable TV and mobile telephony.The long-run average cost per subscriber was used as performance measure andrecommendations were made regarding suitable spectrum allocations and num-ber of licenses to award for personal communication services in the United States.Different cell sizes were used in the cellular network depending on spectrum al-location, leading to varying infrastructure costs per subscriber. Interesting to

2This report was later summarized in a journal article [54].

2.2. Contributions 19

observe is that many of the conclusions and concepts in that study still are veryrelevant, although empirical cost and performance estimates differ from presentlevels.

User deployed access points

User deployed infrastructure has also been proposed as a method to reduce thecost of wireless access infrastructure [55–57]. In [56] the APs are anticipatedto be owned by private users who connect them to their existing broadbandconnections. Access is then granted for everyone, free of charge, or possibly ona reciprocal basis for those that also have local access networks. Another com-mon case of user deployment concerns APs deployed by local operators havingroaming agreements with public operators [58].

Large scale deployment of WLAN was treated in [59, 60] where it, amongother things, was noted that WLAN dimensioning in fact is a 3-dimensionalproblem, and introduced a systematic approach for planning of such systems.In [57], a case study of random AP deployment in offices, shopping malls andcampus areas was conducted. It was concluded that the number of APs requiredfor full coverage roughly could be halved with a planned network instead ofrandomly deployed APs.

2.2 Contributions

Previous research suggests that infrastructure costs can be lowered through BSsthat are well adapted to the deployment case. However, neither a framework foranalyzing the cost structure of heterogeneous networks, nor any quantificationof achievable cost savings, have been found in the literature.

In Paper 1–4, which are reprinted in Part II (Chapters 5–8), we have thereforeinvestigated the cost of using heterogeneous networks to expand the capacity in amobile network. Furthermore, we evaluate how the respective radio access tech-nologies should be developed most effectively as part of a multi-access network.The main contributions of the respective paper are as follows:

1. A methodology is derived for average infrastructure cost modeling underuniform traffic densities, and the cost structure is derived for a few commoncellular BS configurations.

2. The average cost model is extended to the case of non-uniform spatialtraffic densities, in order to assess the cost of heterogeneous networks.Numerical examples are also included with a number of different presentand future radio access technologies.

3. It is shown how the proposed cost model can be used to find the propertradeoff between macro cell radius and AP density, for a mixed 3G andWLAN network. We also introduce the elasticity of infrastructure cost as


an effective method to compare achievable cost reductions with respect toimprovements in both unit cost and performance.

4. Potential cost savings with user deployed APs integrated in mobile net-works are estimated with a random, uniformly distributed, placement ofAPs.

2.3 Delimitations

When assessing the cost structure of a mobile network, a great number of factorsneed to be considered. Proper delimitations and simplifications are thereforenecessary. Network dimensioning is a function of

• Demand – user data rates, area coverage, traffic load, . . .

• Supply – spectrum bandwidth, carrier frequency, spectral efficiency, . . .

In this work, though, we will focus on traffic load. The other variables aremodeled through a feasible average throughput and range for each BS type.

No explicit assumptions will be made on what services that are demanded,except for that only moderate data rates (in the order of 100kbps) are requiredwith full coverage. This is motivated by the rationale outlined in Chapter 1;that is, that only such services with low and moderate data rates should beeconomically viable to offer in mobile networks with wide-area coverage andthat demand for these services therefore should drive network dimensioning.

Quality of Service (QoS) characteristics, such as peak user data rate, outageprobability, blocking probability, and delay requirements are consequently exoge-nous variables. Only aggregate traffic volumes (or average throughput) will beused as measure of demand. In practice, the users willingness to pay for a serviceis (simply put) a function of the value the service brings, competition, and avail-ability of substitutes; see further Appendix A.2. This all varies both spatiallyand over time, and greatly affects a network deployment strategy.3 However, tomake accurate predictions of future demand for specific services and applicationsis very difficult. Moreover, already a few different service mixes would be quitetedious to model and analyze. Therefore, we will in this study resort to havingthe aforementioned “macro-scopic” parameters as a measure of demand.

Moreover, only initial network deployment (roll-out) will be considered.4

This is done to keep the analysis simple and tractable. However, since abso-lute cost estimates are not as important for us as relative comparisons betweentechnologies, this should only have a minor impact on the conclusions. Emergingtraffic demand and the evolution of component cost and performance will hencenot be considered in this initial study.

3For example, a residential user may have access to a flat rate fixed broadband serviceand is thus willing to pay less per bit for mobile data services than a vehicular user (ceterisparibus).

4Normally, network capacity and coverage is increased gradually as demand increases.

2.4. Base Station Characteristics (Paper 1) 21

Strategic marketing issues are also neglected, as well as regulatory require-ments, legacy infrastructure and organizational know-how and culture. Thesefactors will, of course, all heavily affect the deployment strategy for an opera-tor. However, they are outside the scope of this thesis which has a technicalorientation.

All in all, we do not consider the delimited factors as absolutely necessaryfor a first approximation of infrastructure costs. It is still, though, important tokeep these delimitations in mind when making conclusions on viable deploymentstrategies based on the results. If more specific and precise results are required,detailed case studies needs to be performed. Only then can all the aspects listedabove be modeled appropriately.

2.4 Relation between Base Station Characteris-

tics and Cost Structure (Paper 1)

In the first paper we present a model for estimating average infrastructure costsin mobile networks with uniform spatial traffic load. For this purpose, empiri-cally based cost estimates of common BS configurations are derived [23]. Themodels and results are summarized next.

Infrastructure Cost Model

Motivated by the cost structure of mobile networks [1], only the BSs are includedin our cost model. It should be noticed though, that all major auxiliary costsfor a BS site can be included, and not only radio equipment.

With different BS configurations available, the total infrastructure cost C fora mobile operator can thus simply be modeled as

C =∑

i∈B

cini, (2.1)

where ci is the cost for BS type i, ni is the number of BSs that would berequired of that kind, and B is the set of available BS configurations. In thispaper, however, we only consider a uniform traffic distribution and hence onlyone type of BS is considered (for each case).

Network Dimensioning

The network is dimensioned to serve a given average traffic density during the“busy hour”5 and, for the sake of simplicity, only downlink is considered. Thisshould be reasonable since downlink generally limits the aggregate capacity in acellular system, while uplink limits the data rate per link and range (coverage)

5A mobile network is normally dimensioned according to the traffic demanded during thebusiest time of the day – the “busy hour”.


when the traffic load is low [61]. The maximum average throughput (reflectingcapacity) per BS is assumed to be constant, and does not vary as a function ofthe actual cell range or time-varying traffic load, which typically is the case ininterference limited cellular networks.6

With a uniform spatial traffic density the network is thus either limited byaverage aggregate throughput per BS Wmax (“capacity limited”) or the maxi-mum cell range Rmax (“coverage limited”). The number of BSs required is thengiven by

Nbs = max

{

Aservice

πR2max

,NuserWuser

Wmax

}

, (2.2)

where Aservice is the total service area and Wservice is the total aggregatethroughput to be served within that area. Furthermore, the cell radius can notbe smaller than Rmin to assure that inter-cell interference levels are sufficientlylow [61].7

Thus, all required QoS levels need to be feasible at the assumed cell range R(subject to Rmin < R < Rmax) and average BS throughput W < Wmax.

Base Station Performance Assumptions

Performance and cost assumptions for a few typical cellular BS configurationsare summarized in Table 2.1. All values are approximative, but should be repre-sentative in a relative sense for a typical WCDMA system deployed in a WesternEuropean city during 2003. Typical cell ranges and throughput estimates arebased on [61] and do not represent the performance of any specific product. Wehave assumed that the cell capacity is higher for micro and pico BSs than inmacro cells. This since it is possible to minimize inter-cell interference by aproper placement of the antennas (below roof-top or indoors). It should alsobe noted that, for the sake of simplicity, only single carrier WCDMA BSs wereconsidered in this paper and that the macro BS is equipped with three sectors.

The equipment costs estimates were provided by the Gartner Group and theother cost parameters are based on [1]. All cost data were slightly simplifiedaccording to our own assumptions to fit with the chosen system modeling.

Results

The total cost (in present value) for each BS type is presented in Figure 2.2,grouped by:

• Radio – BS equipment and discounted O&M costs.

6In fact, a cellular network is never hexagonally shaped with uniform interference statisticsin practice. However, when modeling systems deployed over large areas it should be reasonableto assume that the average approach we have taken herein is sufficiently accurate.

7Besides, macro BSs can not be too densily deployed due to practical and environmentalreasons.

2.5. Heterogeneous Infrastructure Cost (Paper 2) 23

Table 2.1: Single carrier WCDMA BS performance estimates (based on ownassumptions and [61]).

Macro BS Micro BS Pico BS

Sectors 3 1 1Maximum cell range (Rmax) 1km 0.25km 0.1kmMinimum cell range (Rmin) 0.25km 0.1km 0.025kmSector throughput 0.75Mbps 1.25Mbps 1.75MbpsMaximum BS throughput (Wmax) 2.25Mbps 1.25Mbps 1.75Mbps

• Sites – installation and discounted site leases.

• “Last mile”-transmission – discounted leased line costs.

A more detailed description of the cost assumptions are given in the paper [23],and references therein. In this example micro BSs are 66% cheaper than macroBSs, whereas the cost for a pico BS is only 44% lower than a a micro BS. Thishas a rather intuitive explanation: the equipment cost is lower and site costscan be reduced significantly as the required cell range decreases. However, thetransmission costs are the same for all BSs (which is a somewhat simplisticassumption). Hence, as observed in [41], for BSs with shorter range the cost isdriven by transmission, rather than by radio and site costs.8

The infrastructure cost per user is calculated as a function of user density,for different traffic levels per user during busy hour. With the modeling andassumptions outlined above, results show that single carrier (5MHz) WCDMAmacro BSs are sufficient with less than, approximately, 4Mbps/km2. This corre-sponds to 20 000 voice users (at 20mE), or 400 data users downloading 5MB ofdata during busy hour, per km2. At this traffic, approximately two macro BSsare thus needed per km2, corresponding to a cell radius of 400m. In Figure 2.2it is further shown for what user densities the respective BSs are range limitedunder these performance and traffic assumptions. However, since only a singlemacro-cell carrier was assumed in this example, these values can be be multipliedby three for the initial spectrum allocation typically available for European 3Goperators.

2.5 An Infrastructure Cost Evaluation of Single-

and Multi-Access Networks with Heteroge-

neous Traffic Density (Paper 2)

While traffic was uniformly distributed in the first paper, a spatially heteroge-neous traffic density is instead applied in this contribution [24]. The primary

8The exact figures are of course case specific.


Macro Micro Pico0

50

100

150

200

250

300C

ost [

kEur

o]Base station cost structure

Radio equipment and O&MSite installation and rental"Last mile"transmission

Figure 2.2: Cost structure of typical urban WCDMA BSs calculated in presentvalue over 10 years.

102

103

104

105

101

102

103

104

Users/km2

Infra

stru

ctur

e co

st p

er k

m2 in

pre

sent

val

ue [k

Eur

o]

Average busy hour throughput per user Wuser

= 1kbps

Coverage limited

Pico BS

Macro BS Capacity limited

Micro BS

Figure 2.3: Total infrastructure cost per km2 for different WCDMA BSs withuniform spatial traffic density.


purpose of this is to quantify the cost savings with hierarchical cell structuresand multi-access networks under more scattered traffic variations, both withwide-area and “hot spot” coverage.

Deployment strategies with spatially varying traffic density

With a perfectly homogeneous, uniform, spatial traffic density a single type ofBS will be sufficient to minimize cost. For example, micro cellular BSs may becheapest with high area traffic demand whereas macro BSs typically yield thelowest cost where traffic is low. Hierarchical cell structures and multi-access net-works are expected to be more cost efficient only in scenarios with heterogeneousspatial traffic densities. This is, however, not a sufficient condition; traffic peaks(hot spots) also need to be few and strong. Otherwise a large number of APsare required and there will most likely be excess capacity in the smaller cells.

Heterogeneous Traffic Density Model

User density is in this paper modeled as a log-normally distributed, spatiallycorrelated, stochastic variable.9 This is motivated by traffic distribution in GSMnetworks [63], and detailed population statistics available from the United States[64]. However, the model has not been verified with traffic measurements inmobile data networks.

The traffic map is divided into subareas of 20x20m, where the standarddeviation per sample has been chosen so that local peaks in user density arereached with reasonable probability. Notice, though, that an operator typicallycan not plan the network with that fine granularity in practice, at least notduring rollout when demand is not well known. To fit the standard deviationper macro-cell (with a typical cell radius of 1km) to 0.4dB, reported in [63], aspatial correlation distance of 500m is assumed.

By multiplying the user density with the average busy hour throughput peruser, we get the average traffic to be served. As a reference case we will useprivate speech users, who typically call 1-2 minutes during the busy hour. Thisyields approximately 0.2kbps average throughput assuming a 10kbps voice ser-vice. Furthermore it is assumed that the studied operator has a 30% marketshare, and that overall service penetration is 90%. This gives, for example, anaverage user density of

• 1 350 users/km2 in an urban area with 5 000 people/km2, and

• 5 400 users/km2 in a city centre with 20 000 people/km2.

Based on these numbers the average traffic per user can be approximated.

9Similar to how shadow fading typically is modeled in simulations [62].


0 1000 2000 3000 40000

500

1000

1500

2000

2500

3000

3500

4000

Macro

Micro

Traf

fic D

ensi

ty [l

og10

(Mbp

s/km

2 )]

0.5

0

0.5

1

1.5

2

2.5

3

3.5

Figure 2.4: Example of a network deployment with macro and micro cells cov-ering an area of 4x4km with a spatially non-uniform traffic density.

Summary of Network Dimensioning Algorithm

A simple network dimensioning algorithm has been applied to account for thespatially varying traffic load.10 In essence, macro BSs are deployed first toobtain rudimentary coverage. Micro BSs, Pico BSs, and WLAN APs are thendeployed in hot spots in an increasing order of cell radius. Traffic in area sampleswith low demand (user density) is primarily allocated to the macro cells, and soforth. For the case of fractional (hot spot) coverage only, we include only the,for instance, 20-percentile of traffic with lowest cost per transmitted bit in thecost calculations. Notice that a more detailed description of the algorithm isavailable in the paper.

The gray scale contour in Figure 2.4 depicts a realization of traffic densitygenerated by the model, with a network deployment of macro and micro BSs.Notice that the micro BSs primarily are placed in areas with high traffic densityand that all base stations are placed on regular, hexagonal, grids (with a specificcell radius for each type of BS).

Base Station Characteristics

The system concepts compared in the numerical examples of this paper includethe single-access systems:

10This algorithm was proposed by the other authors of the paper; Magnus Almgren andAnders Furuskar, both with Ericsson Research.


Table 2.2: Performance parameters and cost coefficients for the included radioaccess technologies (BSs, APs and relay clusters).

Radio System Spectral Cell Capacity CostAccess bandwidth efficiency radius [Mbps]

Technology [MHz] [bps/Hz/cell] [m]

3G macro 5-15 0.2 1000 3-9 13G micro 5-10 0.2 250 1-2 0.53G pico 5 0.2 100 1 0.25802.11g 20 1.1 40 22 0.1HSDPA As 3G 0.5 As 3G 2.5x3G As 3G

S3G macro 20 0.75 1000 3x15 1S3G micro 20 0.75 250 15 0.5S3G pico 20 0.75 100 15 0.25S3G 450 20 0.75 2500 3x15 14G macro 100 1 700 3x100 14G micro 100 1 175 100 0.54G pico 100 1 70 100 0.254G relay 100 1 1850 100 6

• WCDMA with Dedicated Channels (WCDMA DCH),

• WCDMA with High Speed Downlink Packet Access (WCDMA HSDPA),

• IEEE 802.11g WLAN,

• preliminary Super 3G (S3G) and 4G proposals,

as well as multi-access combinations of WCDMA DCH/HSDPA and IEEE 802.11g.As in the previous paper, QoS requirements (delay and peak data rates) are notmodeled explicitly. For a fair and relevant comparison the services consideredtherefore need to be feasible to provide with the performance parameters givenin Table 2.2. All performance estimates are based on an outdoor urban environ-ment, whereby resulting costs may be slightly optimistic for indoor services.11

Results

In this paper the total system infrastructure cost, normalized per transmittedgigabyte (GB) of data per month, is compared for different combinations of radioaccess technologies. This has been evaluated as a function of average trafficdensity for both fractional (20%) and almost full (90%) coverage of the offeredtraffic; see Figure 2.5. As a reference level, the traffic volume relative to typicalprivate speech users in a city centre (cc) and low-rise urban (u) environmentsare depicted on the horizontal axes.

11Notice also that the WCDMA performance estimates are slightly different than in Paper 1.


The multi-access concepts IEEE 802.11g WLAN combined with WCDMADCH respective WCDMA HSDPA macro and micro cells yield equal or lowercost than the single-access WCDMA solutions (with pico cells in hot spots).However, gains as compared to pure WCDMA DCH and HSDPA systems areevident only at very high traffic (100 and 300Mbps/km2 respectively).12

Among the studied future system concepts, S3G remains coverage limitedfor slightly higher traffic densities than WCDMA DCH and WCDMA HSDPA,and thus yields lower costs than these systems for traffic densities exceeding2Mbps/km2. Even lower cost can be achieved with the hypothetical S3G 450system; due to its’ large cell radius, the cost is almost 10 times lower than forthe other cellular concepts at low traffic densities. Even for high traffic densitiesS3G 450 is better than S3G, despite the same throughput per BS. This indicatesthat, even with a high mean traffic, there are large areas with less traffic whereBS range still is important for cost efficient operations.

For the case of 20% served traffic only, the main difference is that IEEE802.11g yield a lower cost than WCDMA DCH and HSDPA already at 20 and40 Mbps/km2 respectively (instead of 100 and 300Mbps/km2).

2.6 Cost Efficient Capacity Expansion

Strategies Using Multi-Access Networks

(Paper 3)

In this paper [25], we test different combinations of WCDMA HSDPA macro BSand IEEE 802.11g AP densities to analyze the sensitivity of the infrastructurecost with respect to initial deployment strategy. The evaluation is done with themodeling and assumptions in the previous paper. Furthermore, we introducethe elasticity of infrastructure cost as a measure of how relative improvementsof different sub-systems affect the total cost. That is, to identify what parametersthat are most important to improve in order to reduce the cost of a multi-accessnetwork at different traffic loads. In the perspective of this analysis, we alsodiscuss in the paper how cell range, throughput, and the cost per BS can beimproved.

Elasticity of Infrastructure Cost

Elasticity is widely used in economics to measure the incremental percentagechange in one variable with respect to an incremental percentage change inanother variable [11]. In this study we evaluate the elasticity of infrastructurecost with respect to the cost ci, coverage area Ai, and capacity Wi per AP. Wedefine the elasticity of a parameter X ∈ {ci, Ai,Wi} on the total infrastructure

12Notice that the exact crossover points are subject to the performance and traffic modelingwhereby these results are not generally applicable.

2.6. Cost Efficient Capacity Expansion (Paper 3) 29

10-1 100 101 102 10310 1

100

101

102

103

Traffic Density [Mbps/km2]

Infra

stru

ctur

e C

ost p

er M

onth

per

GB

[Eur

o]

Fraction of Supported Users 20%

u1 u10

cc1

cc10

cc10

0

cc10

00

u100

0

u100

802.11gWCDMA DCHWCDMA HSDCH & 11gHS & 11gS3GS3G 4504G4G relay

(a) 20% served traffic

10-1 100 101 102 10310 1

100

101

102

103


Infra

stru

ctur

e C

ost p

er M

onth

and

GB

[Eur

o]

Fraction of Supported Users 90%

u1 u10

u100

cc1

cc10

cc10

0

cc10

00

u100

0

802.11gWCDMA DCHWCDMA HSDCH & 11gHS & 11gS3GS3G 4504G4G relay

(b) 90% served traffic

Figure 2.5: Infrastructure cost per transmitted GB per month for a few differentsystem configurations, including cellular systems, WLAN, and multi-access com-binations. In the upper graph only 20% of the offered traffic is served, reflectingthe coverage of a hot spot access provider, whereas in the lower plot 90% of thetraffic is served.


Traffic density 1Mbps/km2 10Mbps/km2 50Mbps/km2

HSDPA radius 1000m 800m 400mHSDPA BS density 0.33BSs/km2 0.56BSs/km2 2.2BSs/km2

HSDPA cells/BS 1.4cells 5.7cells 6.8cellsWLAN AP density 0APs/km2 2.1APs/km2 19APs/km2

Infrastructure cost per GB per month:Incumbent operator e6.8 e2.9 e1.7Greenfield operator e8.8 e3.4 e1.9Cost advantage 24% 15% 10%for incumbent

Table 2.3: Summary of monthly infrastructure costs per GB and the cost ad-vantage for incumbents towards greenfield operators.

cost C as:

EC,X =∆C/C

|∆X| /X . (2.3)

Thus, a negative EC,X corresponds to a decreased cost and if EC,X > 0 theinfrastructure cost increases independently of if the changed variable X is in-creased or decreased. The higher absolute elasticity, the greater impact X hason C. Notice that elasticity quite often is calculated in absolute value in eco-nomics [24].

As an example, assume that we want to estimate the elasticity with respectto the area covered per IEEE 802.11g AP. An elasticity of infrastructure costEC,X = −1 would then correspond to that the total infrastructure cost C de-creases with 50% if the cell area is increased with 50%. That is, if the AP rangewas 40 ·

√1.5 = 49m instead of 40m.

Results

In this paper we first estimate the infrastructure cost per GB and month for afew different macro cell radii of an HSDPA system in combination with IEEE802.11g APs. With denser macro cell layer, fewer WLAN APs will be deployed(in accordance with the network dimensioning algorithm described in Section2.5).

Due to the significant difference in cost and range of WCDMA HSDPA macroBSs and IEEE 802.11g APs, different cell radius in HSDPA will minimize costat different traffic densities; see further Figure 2.6. With the assumptions andmodeling applied, we see that the incremental cost per GB and month flattensat approximately 4Mbps/km2. However, more macro cells are still beneficial astraffic increase. Otherwise too many APs will have excess capacity in areas withmedium traffic. The results are summarized for a few traffic densities in Table2.3.

2.6. Cost Efficient Capacity Expansion (Paper 3) 31

100 101 102

100

101

102

Average Traffic Density [Mbps/km2]

Infra

stru

ctur

e C

ost p

er M

onth

and

GB

[Eur

o]

Voice 10 x voice 100 x voice

200m400m800m1000m

Figure 2.6: Infrastructure cost per GB and month for an incumbent operatorwith a multi-access network consisting of WCDMAHSDPAmacro BSs and IEEE802.11g APs. The curves depict different cell radii in the macro cells.

The elasticity of infrastructure cost analysis shows that HSDPA capacity isslightly more important to improve than 802.11g coverage with a dense macro cellnetwork (400m cell radius). However, with 800m cell radius improving HSDPAcapacity yields twice as high cost reduction as 802.11g coverage which is shownin Figure 2.7. However, for traffic densities above 50Mbps/km2 the result is theopposite. Furthermore, as seen in Figure 2.7, half of the total cost stem fromeach sub-system at approximately 10 and 100Mbps/km2 with 800m and 400mcell radius in WCDMA HSDPA respectively. Thus, the benefits of improvingdifferent subsystems greatly depend on the initial dimensioning of the cellularsystem and the average traffic density.

Positioning of Future Radio Access Technologies

These results points at how a future 4G radio interface targeted for urban envi-ronments could be differentiated with respect to current main stream technolo-gies for wireless data connectivity. Examples of base station configurations notcovered well by today’s systems (for urban deployment) are, as we see it, highcapacity micro BSs and long range WLAN APs.

Regarding the BS cost structure we can also note that, given the empiricaldata available and our assumptions, macro cells are dominated by site rent andinstallation. As observed in [18], this is probably difficult to reduce, both in ur-


100 101 102

1

0.5

0

0.5

1


Ela

stic

ity o

f Inf

rast

ruct

ure

Cos

t


HSDPA costHSDPA capacity802.11g cost802.11g coverage

(a) 400m HSDPA cell radius

100 101 102

1

0.5

0

0.5

1


Ela

stic

ity o

f Inf

rast

ruct

ure

Cos

t


HSDPA costHSDPA capacity802.11g cost802.11g coverage

(b) 800m HSDPA cell radius

Figure 2.7: The figure shows the elasticity of infrastructure cost for an HSDPAand IEEE 802.11g multi-access network with respect to improved AP costs,HSDPA capacity, and 802.11 range respectively. In the upper figure, the cellradius of HSDPA is 400m (adapted for 100Mbps/km2) and the lower graphdepicts the results for a 800m cell radius (suitable for 10Mbps/km2).

2.7. User Deployed Access Points (Paper 4) 33

ban environments with constantly increasing property prices, and in rural areasdue to the construction work. WLAN APs are, instead, almost completely domi-nated by “last-mil” transmission. Considering indoor deployment, which is mostcommon for WLAN, we could therefore foresee novel, distributed, deploymentstrategies involving the users and that is the focus of the next paper.

2.7 On the Cost Efficiency of User Deployed Ac-

cess Points Integrated in Mobile Networks

(Paper 4)

An increasing availability of fixed broadband networks, including digital sub-scriber lines and cable modems, and the development of WLAN technology willenable new designs of public wireless access networks. In this paper [26], theeconomics of user deployed APs that are open for other subscribers and roamingpartners is considered. More specifically, we calculate the infrastructure costas a function of traffic density (area capacity) for different mixes of operatordeployed BSs and user deployed APs. Furthermore, the number of APs requiredto serve different fractions of the offered traffic is estimated.

Network Franchising

Integrating user deployed APs in a public mobile network has, to the best ofour knowledge, not yet been implemented in practice. Use cases and businessmodels for that is an interesting topic, and we envisage that this is a plausibleextension of the ongoing convergence between fixed and mobile systems. Onepossible business model that could be adopted by operators interested in exploit-ing this possibility would be “network franchising”; meaning that users installAPs, which the operator controls in terms of access rights, etc.

A successful franchising agreement of course relies on that both parties ben-efit. In this case, the operator obtains accessibility to APs providing cheap,high-capacity, wireless access whereas the user gets an AP and some compensa-tion by the operator. The operators could further on compensate the AP ownerthrough bundling of different services, such as fixed broadband, subsidized accessboxes, and wireless access when the user is in other locations. These marketingrelated issues are however outside the scope of this paper. Yet, we can note that“network franchising” in particular could be of interest to MNOs with limitedspectrum and/or poor indoor coverage, or broadband providers that would liketo exploit their fixed network by offering wireless access (indoors).

Network Dimensioning with User Deployed Access Points

In the operator deployed systems modeled in the two previous papers [24,25], BSswere deployed in a decreasing order of cell range on hexagonal grids. Herein, we


0 5 10 15 20 25 30 35 4010

20

30

40

50

60

70

80

90

100

Percentage of subscribers with APs

Per

cent

age

of tr

affic

cov

ered

20mE voice traffic100 x voice200 x voice

Typical fraction of traffic terminated indoors in today's mobile systems

Figure 2.8: Fraction of traffic covered as a function of the percent of users withan open AP, for different data volumes.

will also introduce user deployed APs in the analysis. In that case, user deployedAPs are instead deployed first, in a random fashion with a uniform distribution.Residual traffic is then allocated to operator deployed BSs according to theprevious algorithm.

All cost and performance assumptions for WCDMA HSDPA and operatordeployed WLAN (IEEE 802.11g) are the same as in [24,25]. User deployed APs,instead, resembles IEEE 802.11b with 50m range and 5Mbps average throughput.Moreover, the cost for a user deployed AP is 20 times less than for a operatordeployed WLAN AP, including a revenue sharing of approximately e100 per yearwith each AP owner. Moreover, the cell radius of HSDPA is 1000m for trafficdensities below 5Mbps/km2, 800m between 5 and 20Mbps/km2, and 400m fordensities above that [25].

Interesting to note is that if 1, 2 or 4% of the subscribers install open APs,as much as 30, 40 and 70% of the traffic, respectively, is covered by these APs;see Figure 2.8. This indicates that user deployed APs could bring substantialcost savings with respect to traffic dimensioning.

Results

As in the two previous papers, we have herein evaluated the infrastructure costper GB per month, but this time for a few levels of user deployed APs (measuredas the percentage of subscribers with APs). As expected, the operator deployed

2.8. Conclusions 35

100 101 102

100

101

102


Mon

thly

Infra

stru

ctur

e C

ost [

Eur

o/G

B]

HS+11gHS+11g+AP1%HS+11g+AP2%HS+11g+AP4%

Figure 2.9: Monthly cost per transmitted GB as a function of average traffic den-sity. The solid line is for an operator deployed multi-access network with HSDPAand 802.11g. The other lines represent different percentages of subscribers withopen APs.

network yields lowest cost at low traffic densities whereas user deployed APs isworthwhile introducing at approximately 10Mbps/km2; see further Figure 2.9.At ten times that traffic density, the cost for operator deployed networks haveflattened (as seen in the previous paper), whereas the incremental cost per GBand month still diminishes if instead more APs are deployed by the users.

2.8 Conclusions

In this chapter we have compared a few alternative methods to reduce the costof mobile data networks using various combinations of radio access technologiesand BSs. In particular, we have compared multi-access networks consisting ofWCDMA and WLAN, with conventional single-access hierarchical cell struc-tures. Future network concepts (S3G, 4G, etc.) have also been considered, aswell as user deployed APs.

For this purpose, we proposed a model to estimate the cost of a radio accessnetwork as a function of traffic density. The model is based on average costand performance data, and accounts for both investments and running costs(per BS). To dimension the network we utilize a statistical model for geograph-ically varying traffic demand. No explicit QoS-requirements or service mixesare assumed, instead, the maximum feasible cell range and average throughput


ultimately determines what services that the network is capable to deliver. Thekey numerical results are summarized next, followed by a discussion of the needfor multi-access networks and factors that affect the validity of the presentedfindings.

Infrastructure Cost Structure

We have seen that the cost structure of a wireless system largely is a functionof the characteristics of the deployed BSs. For a macro BS the costs mainlyconsiders BS equipment, O&M, installation and site rent, whereas “last mile”-transmission dominates for pico BSs and operator deployed WLAN.

Multi-access networks, composed of WCDMA macro and micro cells com-bined with IEEE 802.11g WLAN APs, do not bring lower costs per user thansingle-access networks. On the other hand, there is no loss either. From aninfrastructure cost perspective, there should hence be no major disadvantage foran MNO to introduce WLAN instead of pico BSs in “hot spots”. Amongst thehypothetical future network concepts studied, the overall lowest cost is enabledby a S3G system operating in the 450MHz band. The long range, in combina-tion with a high throughput, is thus beneficial also at high average traffic loads.Provided that wide-area coverage is demanded for the considered services, wecan thus conclude that range also is important to improve (or at least maintain)if traffic demand increases, and not only aggregate throughput per BS.

We have also illustrated how the elasticity of infrastructure cost can be usedto analyze what design parameters, including both performance and costs of dif-ferent systems, that are most important to improve in a multi-access network. Inan example, we probed a bit deeper into the case of a combinedWCDMAHSDPAand IEEE 802.11g network. For this specific case, the aggregate throughput perHSDPA macro BS was shown to be more important to improve than the range ofIEEE 802.11g in the case of a dense macro cell network (with 400m cell radius).If, instead, HSDPA BSs are more sparsely deployed (800m cell radius), similarcost savings can be achieved by increasing the range of IEEE 802.11g already atmoderate traffic loads. Thus, what parameter to improve in different subsystemscan not be generalized; it depends on the original network design and the levelof traffic demand.

Despite that hierarchical cell structures and multi-access networks reducecost, however, the incremental cost per GB always flatten at some traffic loadfor operator deployed systems (for a given level of technology). Incorporatinguser deployed APs in the network would then be an attractive method to in-crease capacity even further at a (still) diminishing incremental cost. With afew numerical examples, for different fractions of users equipped with open APs,we showed that the overall infrastructure cost can be reduced significantly. Thiseven with a considerable amount of revenue sharing with the AP owner.

2.8. Conclusions 37

The Need for Multi-Access Networks

Due to the minor difference in cost we have observed for a multi-access net-work as compared to single-access systems, it is plausible that other factorsthan aggregate traffic volumes in the end will have greater impact on an MNOstechnology strategy. Thus, as described in Section 2.3, a thorough investmentanalysis is required to make recommendations on an infrastructure deploymentstrategy. A few general reflections could still be made, to put the presentedresults in a broader perspective.

While advantages with a multi-access deployment strategy include increasedpeak user data rates, less need for licensed spectrum and reduced investments,a drawback is that it typically is more complicated to manage multiple systems(both in terminals, and in the central systems).

Furthermore, already the initially deployed WCDMA networks have a cellradius of a few hundred meters in urban areas. This was necessary in order toobtain sufficient indoor coverage for services like video streaming and lap-topconnectivity with data rates in the order of 64–384kbps. Thus, UMTS networksare in practice quite often coverage limited up to quite high traffic densities alsoin urban areas. The need for additional WLAN and cellular micro and picoBSs in such scenarios should hence primarily be to improve coverage for higherdata rates in specific places, for example in malls and large office buildings, andnot for capacity reasons. For greenfield deployments, however, a multi-accessnetwork could be considered from the beginning and thereby enable a sparsermacro-cell network. Of particular interest should then be the opportunity tointroduce user deployed APs in the network. Still, WLAN in general, and userdeployed APs in particular, may not be sufficient for services that require goodcoverage, reliability, and QoS. In that case, a mobile network is still useful forrudimentary coverage and capacity.

Validity of Results

To conclude this chapter, we will discuss a few important assumptions andchoices of modeling which could affect the accuracy of the presented results.Firstly, all numerical results are subject to our specific assumptions. Althoughthe intention has been to use fair and realistic parameter settings, these may ofcourse differ significantly for a real deployment case.

The spatial traffic model was derived from population statistics and trafficmeasurements from GSM networks. It is not certain that mobile data userswill have a similar behavior, even though that may be a reasonable assumptionfor the (still) moderate data rates being the focus of this work. Therefore,empirical data on traffic demand for mobile data services would (when suchbecome available) be useful to improve the heterogeneous traffic density model.

Furthermore, we did not model QoS requirements explicitly, and the resultingdata rates, etc., could therefore vary significantly between compared systemconfigurations. Besides, no attempts have been made to optimize the network


deployment. Instead, the target has been a simple principle that is reasonablygood and fair between system concepts. Still, although we elaborated on differentcell radii in Paper 3 [25], a more extensive sensitivity analysis is of interest forfurther studies.

Moreover, the network dimensioning model is primarily not intended for highrise buildings, where a 3-dimensional model is needed. This naturally limits thescope of the study at hand. It should also be noted that the user deployed APswere uniformly distributed. This may be a pessimistic assumption for estimatingpopulation (or household) coverage, since more APs “automatically” will beplaced where many people live. However, high population peaks are most oftenwhere there are high-rise buildings so with our 2-dimensional model, the resultwould be too optimistic if APs are deployed proportional to traffic demand.

Regarding cost estimates, we have relied on secondary data in almost allcases. However, also here, the ambition has been to model the relative differ-ence between technologies, in an average sense, as fair as possible. It shouldbe stressed though, that a linear annualization of CAPEX would be even morestraightforward than the present value calculations we have utilized, and prob-ably yield similar results.13

Yet, in spite of the simplistic assumptions used herein, the proposed method-ology should, much thanks to its simplicity, be useful for an initial assessmentof operator deployment strategies and when technical requirements are definedfor future radio access technologies (for instance in standardization bodies).

13In fact, we would recommend a linear annualization as a base line assumption for furtherstudies in this area.

Chapter 3

Multi-Operator ResourceSharing

Herein, we will consider three different cases of multi-operator resource sharing,which differ greatly from a business perspective but have quite a lot in commontechnically.

Firstly, network sharing has evolved as an important method to reduce in-vestments in wireless infrastructure, especially in rural areas for systems andservices with limited link budgets. This can, as discussed further in AppendixB and in the following literature study, either be implemented as a commonshared network or by means of geographical sharing. Secondly, the market forMVNOs who, with little or no mobile infrastructure, offer wireless services issteadily increasing. Thirdly, MNOs could potentially agree to allow for roamingin between their existing networks in order to increase coverage and capacity forhigher data rates (hereinafter denoted national roaming). The common technicaldenominator for these flavors of network sharing is that they can be facilitatedwith functionality originally designed for international roaming. Therefore, wewill refer to them jointly as roaming based network sharing.

A potential drawback with roaming based network sharing is that forecast-ing of traffic demand could become less transparent to the network provider.Hence, given that over-dimensioning remains expensive, the post-paid chargingof MVNOs (see further Appendix B.2) may be insufficient for network planningpurposes. Furthermore, problems could arise in terms of free-rider effects andthere is a risk that competing service providers provide consciously misleadingtraffic forecasts. Thus, besides assuring certain QoS levels for the roaming part-ners, those should also be “punished” in some way if the actual traffic exceed thecontracted volumes significantly and this topic will be addressed in this chapterof the thesis.

Moreover, we will investigate what performance benefits that can be expectedfor data services by sharing overlaid cellular networks via national roaming. Al-

39

40 Chapter 3. Multi-Operator Resource Sharing

though that was technically feasible already in earlier generations of mobilesystems it has, to the best of our knowledge, not been implemented in practiceso far. Partly because of the limited bandwidth required for voice telephony,but also due to competitive reasons; coverage has simply been seen as a majorcompetitive advantage [28]. However, if the focus of operators and competitionauthorities shift towards applications and services, and due to the inherent dif-ficulties to provide wide area coverage for higher data rates, national roamingmay be worthwhile to reconsider for future radio access. In particular whenintroducing new bandwidth demanding services, for which demand is uncertain,that require significant area coverage. National roaming could in that case evenbe of interest for the MNOs in order to reduce upfront investments (and, thus,their risk exposure).1

3.1 Related Work

Previous studies on network sharing mainly considers techno-economical aspects,including cost savings [1, 27, 29], competition [30, 65], and identification of newrequirements on network management [66, 67]. While fair resource sharing hasbeen addressed in numerous papers, both for fixed and wireless networks, pre-vious work have mainly considered individual connections and service classessharing a common link. Besides this, some initial work on fair resource sharingin multi-operator networks have recently been presented [68]. Moreover, spec-trum sharing has recently appeared as part of the ongoing research targeting4G [69,70].

Potential Cost Savings

The financial benefits of network sharing were analyzed in [1, 27–29]. Thesestudies show that cost savings are substantial, in particular in rural areas wherecapacity utilization is low. In areas with higher user density the cost per sub-scriber is sufficiently low with single-operator networks, and the drawbacks withnetwork sharing (reduced differentiation possibilities, administrative overhead,etc. [27]) are thus not justified solely by cost savings.

In the short run, though, network sharing could still be used to shorten thetime to market for new services and radio access technologies using, for example,geographical sharing. If demand surges the operators can expand their networksto provide full coverage.

1Notice that for niche service providers requiring high data rates and reliability, for examplefor video surveillance cameras, this could of course also readily be implemented by usingmultiple subscriptions.


Competition

Competition amongst sharing UMTS operators was studied in [30,65], focusingon the Swedish market. A key competitive advantage for MNOs has been toprovide good area coverage. With network sharing, other differentiation oppor-tunities are hence needed. Quite a few possibilities were identified in [30, 65],of which investing in multi-access networks was the most important2, togetherwith services that can be implemented in the unshared domain (that is, in thecore network and above). Still, most services require modifications also in theshared RAN whereby site sharing is the only level of network sharing which doesnot severely limit the operators’ differentiation possibilities [30].

Some drawbacks and difficulties with geographical sharing were also outlinedin [27]. This study emphasized that

• Marketing campaigns launched by competitors may boost traffic load be-yond the current network capacity, resulting in severe blocking for yourown customers.

• Customer driven coverage3, which is quite common at enterprises, mayonly be provided by the operator responsible for that area.

• Network quality needs to be sufficient for your customers and the servicesyou promote.

All in all, both common shared networks and geographical sharing will, with-out further considerations, cause both administrative and competition relatedproblems [27–30, 65]. These, and more practical technical problems, have alsobeen addressed in the standardization body behind 3G4, and the standards forUMTS have been updated accordingly to support the most fundamental fea-tures of shared networks [66, 71]. This includes operator specific neighbor celland access rights lists, display of the home operator name in the terminals, etc.However, as pointed out in [30], there are many aspects hidden in the detailedconfiguration of a cellular network which also limits differentiation possibilities.

Service Level Agreements

A so-called Service Level Agreement (SLA) between the service provider andhosting RAN provider should include the requirements and responsibilities forboth parties, considering for example QoS levels, reliability, performance moni-toring, customer support, pricing policies, etc. [72,73]. An exhaustive recommen-dation for telecommunication SLAs has been provided by the TelecommunicationManagement Forum [74].

2In order to limit the scope of the shared UMTS network, the coverage of alternativetechnologies such as WLAN and EDGE could be expanded.

3Operators sometimes deploy special indoor solutions to improve coverage and capacity fortheir corporate subscribers.

4Third Generation Partnership Program (3GPP)


While the short-term fulfillment of the terms in an SLA is handled by RRM,the service level monitoring and long-term actions to assure the contracted QoSlevels are primarily a task of network management [73]. The latter also includesthe procedure of mapping traffic forecasting onto needs for network capacity.There are, however, no well established methods or processes for this so far [73].

In order to share the risk with large MVNOs, the access provider couldpotentially base inter-connection charging on the share of the network capacitythat the MVNO is granted (similar to a network sharing agreement) [29]. Pricingmodels for this scenario was also discussed in [72], where the author outlined afew parameters that should be included in an SLA.

Fair Resource Sharing Between Operators

The resource allocation problem for shared cellular networks was recently treatedin [68]. Two principle methods to allocate radio resources fairly between sharingoperators was considered for a WCDMA downlink system. One with fixed powerallocations, which reduce trunking efficiency, and one based on adjusting elasticdata rates for packet switched bearers. None was found to improve fairnesssignificantly as compared to a reference system without any specific sharingmechanism, although lowering data rates for the operator with highest load atcongestion gave a slight improvement.

Spectrum sharing between cellular operators was investigated in [69,70]. Forthe case of two cellular operators sharing the same frequency band it was con-cluded that trunking gain leads to increased capacity. However, the gain van-ished with displaced BSs due to near-far effects. Notice that, in this thesis,we will only consider the case where multiple operators fully share the samenetwork.

The general problem of allocating radio resources to different bearer classeswas treated in [19]. Fair sharing between individual connections sharing the samewireless system has also been treated in numerous papers considering packetscheduling between connected bearers. The objective of these studies is typi-cally to maximize system capacity, often measured as the number of supportedusers [19], while assuring the QoS for individual connections. This problem isa tradeoff between the bearers’ resource consumption, delay and throughputrequirements. To not complicate that further, by adding a constraint on howmany radio resources that should be allocated to each operator, we will insteadtry to solve the problem of fair resource sharing between operators already inadmission control.

3.2 Contribution

Radio resource sharing between operators is an interesting option to reduce in-frastructure costs. With roaming based network sharing a more formal structurewith negotiated capacity requirements between the service and RAN providers

3.3. Fair Resource Sharing (Paper 5) 43

could be beneficial. In Paper 5 and 6, we have contributed to a frameworkfor radio resource sharing in multi-operator networks and analyzed the techni-cal performance benefits with national roaming. The papers are reprinted inChapter 9–10, and the main contributions of the respective paper are as follows:

5. A basic algorithm for fair resource sharing was proposed and evaluatedwith a simple queuing system model. The algorithm is based on non-preemptive priority queuing in the admission control, and thus no capacityhas to be reserved to achieve fairness (at the expense of increased call setuptimes).

6. The achievable user data rates with national roaming between equivalentoverlaid cellular networks are estimated using a statistical model for uplinkbest effort data users. In particular, the effect of operator specific BS sitesis studied, and for this purpose a model for inter-operator site distance isintroduced.

3.3 Radio Resource Management in Roaming

Based Multi-Operator WCDMA networks

(Paper 5)

In this paper [32], we introduce the problem of fair resource sharing in multi-operator cellular networks, and propose a load based priority queuing in theadmission control to keep down the probability of call blocking for operatorsthat have not reached their agreed load. The focus is on “roaming based shar-ing”, which means that an operator access another operators RAN indirectlyvia the core networks. This implies that multiple operators fully share the sameRAN, which motivates a radio resource control between the operators. Normallythe operators share the same carrier(s), but it is also possible to use dedicatedcarriers.

Besides roaming based sharing, there are today two other major categoriesof network sharing, being RAN sharing and site sharing. The three groups ofsolutions imply different levels of sharing, which is depicted in Figure 3.1 (seefurther Appendix B).

Methods for Allocating Radio Resources

How much of the radio network capacity that each sharing partner has the rightto use with roaming based sharing is commonly specified in an SLA. An operatorthat follows its terms in the SLA should receive the agreed QoS levels; this evenif the other operators try to utilize more than agreed capacity.

This implies that radio resources must be shared in a controlled way betweenthe operators and this can in principle be achieved by:


Core network

Radio access network

BS BS BS BS

Roaming based sharing

Site sharing

RAN sharing

Figure 3.1: The figure illustrates what levels of a cellular network architecturethat different sharing methods relate to.

• using dedicated carriers for each operator,

• allocating a fixed capacity share for each operator per carrier, or

• dynamically prioritizing users from different operators (within one or mul-tiple carriers).

Dedicated carriers and fixed capacity shares provide fairness between operators.Unfortunately, though, any capacity reservation scheme implies a reduced ca-pacity utilization (trunking efficiency). Instead, a dynamical prioritization ofoperators based on the current load is preferable. For this purpose standardRRM functionality such as admission control and packet scheduling can be uti-lized. In this paper we limited the scope to admission control, which in thiscontext is responsible for admission of new connections (both packet and circuitswitched) [61].5 Furthermore, assuming that a minimum rate is required for alladmitted bearers, we will without loss of generality limit the study to circuitswitched traffic.

Queuing System model

For the analysis in this paper a standard queuing system model with Poissonarrivals will be used [75]. The total offered load per operator i is denoted Oi, andis defined as Oi = λiT where λi is the average arrival rate of new connectionsfor operator i and T is the average duration per connection. The total offeredload is then given by

O =

N∑

i=1

Oi, (3.1)

5Packet scheduling, on the other hand, is responsible for adjusting the bit rate and thusresource consumption of connected non real-time radio bearers. Fair sharing using elastic bitrates was studied in [68].

3.3. Fair Resource Sharing (Paper 5) 45

assuming that N operators share the network. Each of these operators haspriority to Ci channels per cell. A new connection request is queued until thereis a channel available. However, if the waiting time Td exceeds a certain thresholdTmax the request is blocked.

The total number of channels per cell C =∑

Ci is thus modeled as constant,which is a simplistic assumption for an interference limited system. However, foran initial assessment of a priority queuing method, we believe that the allowedqueuing time, average connection duration, and the total number of channelsper cell will have stronger impact on the performance. More detailed systemmodeling is left for further studies.

Admission control with Non-Preemptive Priority Queuing

As a basic approach to solve the fair sharing problem we propose to use admissioncontrol with non-preemptive priority queuing. Connection requests of differentoperators are prioritized according to the respective operators’ load level relativeto their agreed capacity.

Preemption, that is, allowing for removal of existing connections to makespace for a new request with higher priority will not be exploited. Althoughpreemption would increase fairness, it would increase the probability of droppingactive connections.6

An overview of the applied admission control algorithm is depicted in Figure3.2. The priority level of each operator (Pi) is defined as

Pi =Ci

Li

, (3.2)

so that operators with a load Li lower than the agreed minimum capacity Ci

receives a high priority. Li is simply defined as the total number of allocatedchannels for that operator at a given point in time (without any averaging).

The queuing management outlined here can for example be implemented inconjunction with a periodical admission control and it consists of the followingsteps:

1. A new connection request that arrives when the system is congested (thatis, when

∑

Li = C) is put in the queue.

2. The queue is periodically sorted in a descending order according to Pi.Then each operator’s connections are sorted group-wise in a descendingorder based on Td. Consequently, the operator with highest Pi will beserved first and each operator’s connection requests are served in a first-in-first-out (FIFO) manner relative to each other.

3. If a channel has been released, the first user in the queue is admitted.

6In general this is considered to be the most important QoS measure for circuit-switchedservices (like voice telephony).


Put new connection request(s) in queue

Channel available?

Sort queue: 1) Pi 2) Td

Allocate channel Connection blocked!

Td>Tmax?

Yes Yes

No

No

Figure 3.2: Flowchart of periodical admission control with non-preemptive pri-ority queuing.

4. Connection requests for which Td > Tmax are blocked and removed fromthe queue.

Results

As a measure of the performance of the algorithm, we consider to what extentoperator specific blocking probability Bi can be kept below some given thresholdBmax (say 5%) if the load Oi < Ci. Hence, as long as an operator has notexceeded its agreed load share, their users should experience the contractedblocking probability.

In an example with two operators sharing a network 50/50, we show thatthe proposed algorithm functions well for a system with C = 80 channels percell. This case should resemble an urban WCDMA macro cell with voice usersonly. A queuing time Tmax = 5s is allowed for new connection requests. Withonly 16 channels per cell, modeling a video streaming service in WCDMA, thealgorithm is less effective even though Tmax was increased to 15s. The operatorspecific blocking probabilities are plotted as a function of traffic load for bothsystems in Figure 3.3.

3.4 An Estimation of the Achievable User

Throughput with National Roaming

(Paper 6)

As discussed previously, despite the introduction of advanced transmission andpacket scheduling techniques, existing mobile data networks would require sig-nificant investments in order to support higher data rates with wide area cov-

3.4. Throughput with National Roaming (Paper 6) 47

20 30 40 50 60 70 800

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Offered load for operator 1 (O1) [Erlang]

Blo

ckin

g p

robabili

ty for

opera

tor

1 (

B1)

Fixed reservationO

2 = 80 Erl

O2 = 60 Erl

O2 = 40 Erl

O2 = 20 Erl

(a) Speech system with 80 channels per cell

2 4 6 8 10 120

0.05

0.1

0.15

0.2

0.25

Fixed reservationO

2 = 14 Erl

O2 = 10 Erl

O2 = 6 Erl

O2 = 2 Erl

Blo

ckin

g p

rob

ab

ility

fo

r o

pe

rato

r 1

(B

1)

Offered load for operator 1 (O1) [Erlang]

(b) Video streaming system with 16 channels per cell

Figure 3.3: The figure depict the blocking probability of operator 1 (B1) as afunction of offered load (O1) for different levels of load for the other operator(O2). In the upper graph, a speech service is assumed whereas the lower graphshow the results for video streaming. The dashed lines represent a fixed resourceallocation of half (C/2) channels per operator.


erage. Consequently, many operators have chosen to postpone the necessarynetwork upgrades until consumer demand becomes more pronounced. Althoughthis might seem sound at a first glance, this is definitely not the case since sup-porting high-end users (“early adopters”) has proven to be a crucial enabler forreaching a mass-market later on [76,77].

An alternative method to increase coverage for higher data rates withoutadditional investments in hardware would instead be to allow users to roambetween existing networks with, at least partially, overlapping coverage; hereinreferred to as national roaming.7

In this paper [33], we investigate what throughput gains users can expectwith roaming between multiple overlaid cellular networks. Moreover, we studiedto what extent these gains depend on the microscopic diversity order and the rel-ative location of the operators’ BSs. The study is limited to uplink transmission,since that typically limits coverage in cellular systems.

Base Station Deployment Model

A multi-operator environment with J = 3 operators is investigated and usersare either allowed to connect to their own operators network only, or to any ofthe J cooperating operators’ BSs. For the sake of simplicity all operators areassumed to deploy similar BSs on a hexagonal grid with cell radius rc. Eachoperator has one carrier frequency, which is used in every cell (reuse factor 1).

The relative position of the sites belonging to different operators is capturedby the inter-operator site distance (d), which we define as the minimum distancebetween adjacent sites belonging to different operators. Thus for a scenario withJ operators,

d = min(

d1, d2, ..., d(J

2)

)

. (3.3)

Herein, we will consider the special case when d = d1 = . . . = d(J

2). For this case,

with three-operators, the relationship of the operators’ site locations is given by

(

xi

yi

)

=

(

x0

y0

)

+ d

(

cos(π/6)(−1)isin(π/6)

)

, (3.4)

where i = 1, 2 and (x0, y0)Tis the position of the reference network. Figure 3.4

depicts an example where d = rc.

Summary of Cellular System Model

More detailed system modeling and parameters are given in the paper, butfor convenience the utilized models are summarized next and the main system

7Notice that geographical sharing also has been called national roaming in the literature.Although the cases are different, a common denominator is that operators allow users fromother operators to roam in their networks and that they retain the possibilities to expand theirown network capacity if demand surges.

3.4. Throughput with National Roaming (Paper 6) 49

1500 1000 500 0 500 1000 1500 2000

1500

1000

500

0

500

1000

1500

7

d1

rc

2

3

d

d

Figure 3.4: An example of the BS deployment for a three-operator scenario andinter-operator site distance d = rc=360m. It should be noted that the maximumvalue of d is periodic with a period 2

√3rc

parameters are provided in Table 3.1. To start with, all users are stationary anduniformly distributed over the service area. This results in a Poisson distributedtraffic per operator and cell. Moreover, for simplicity reasons, we assume thatall users have full buffers and thus have data to transmit all the time.

Propagation channel modeling is chosen to mimic an urban macro-cell en-vironment, with distance dependent path loss according to the COST231-Hatamodel [78]. Shadow fading is log-normally distributed with 8dB standard devia-tion and 20m correlation distance [62,79]. Rayleigh fading is also included, andthe fading component is independent for each receive antenna (we will use twoand four antennas). Moreover, for the sake of simplicity, there is no multi-pathfading.

The average transmit power was multiplied with the number of users con-nected per cell to compensate for reduced transmission time. However, the peaktransmit power was limited to eight times the average in order to have a limiteddynamic range. Two different packet schedulers have been studied, in order tovary the degree of diversity gain against Rayleigh fading. These were:

• “Round Robin” scheduling, with time-hopping to randomize interference,and

• “Proportional Fair” scheduling of the user with instantaneously most fa-vorable fast fading.


Table 3.1: System Parameters

Parameter Value

Population density [km−2] 5000# Operators 3

Slot duration [ms] 2Correlation distance [m] 20

Standard deviation (shadow fading) [dB] 8Shadow fading correlation between BSs 0.5

Pathloss@1m [dB] 35.8Distance dependent attenuation factor 3.5

Cell radius [m] 360Channel bandwidth [MHz] 5Thermal noise floor [dBm] -103# Receiving antennas 2, 4

Average terminal power [dBm] 24Maximum terminal power [dBm] 33

# Sectors per BS 3Traffic [users/cell] 1, 3, 10

Inter-operator site distance [m] 0–623.5

User throughput is estimated using a truncated Shannon model, where themaximum data rate was selected so that approximately 10% of the users reachthis rate in the single operator case (with the modeling and assumptions used;20Mbps).8 The resulting SIR with maximum ratio combining is calculated asthe sum of SIR (including thermal noise) over each antenna branch [80].

While Round Robin scheduling is used as a reference case, the ProportionalFair scheduler will in conjunction with four receive antennas model a systemwith a high degree of diversity against Rayleigh fading.

Results

The gain with national roaming was in this paper presented with respect to thelower 10th percentile and average user throughput; see further Figure 3.5. Thedifference in throughput is largest for the 10th percentile, for which nationalroaming increase user throughput as compared to the single operator referencesystem with approximately 80-190% with two receive antennas and Round Robinpacket scheduling. It is interesting that an 80% gain is obtained already whenBSs are just slightly separated. This can be explained by diversity gain against

8This modeling is motivated by i) that modern radio access systems operate very closeto the well known Shannon bound at low mobile velocity, and ii) that a system typically isdesigned so that the maximum rate is reached with some reasonably low probability.

3.5. Conclusions 51

shadow fading.9 Average user throughput is increased with approximately 20-50%, and a similar dependency on inter-operator site distance can be observedas for the 10-percentile.

With four receive antennas and proportional fair scheduling, which gives ahigher order of diversity gain against Rayleigh fading, the relative increase inthroughput is smaller (although still significant). The gain in user throughputis in this case between 50-100% for the 10-percentile whereas the average userthroughput is increased with approximately 15-30%. The reduced gain with ahigher order of diversity gain can be explained by the logarithmic rate function(with a higher SIR, the systems could have exploited a wider bandwidth).

3.5 Conclusions

Motivated by arising business needs, we have in this chapter first outlined howfair resource sharing can be assured for operators using the same RAN. For thispurpose, an admission control with non-preemptive priority queuing based onoperator specific load was proposed. With a few numerical examples, we showedthat the method is promising for systems with a large number of channels percell. In this case, a quite moderate queuing time is sufficient. For systemswith fewer users per cell, stricter methods exploiting resource reservations orpreemption of allocated bearers would be necessary for a fair resource sharing.However, this would come at the expense of decreased capacity utilization orincreased probability of dropped calls.

The benefits with national roaming between overlapping macro cellular net-works were also assessed for uplink data services. It was shown that user through-put can be increased significantly already with adjacent BS locations, and inparticular for users with high path loss. Thus, national roaming promises largecoverage gains with only minor incremental infrastructure costs. However, therelative gains will be smaller if the system already operate at high SIR levels.

Discussion and Validity of Results

Simplistic statistical modeling have been used for the numerical evaluation inboth Paper 5 (Fair Resource Sharing) and Paper 6 (Throughput with NationalRoaming). Thus, the numerical results presented herein are indicative onlyand the performance in real systems may differ significantly from these. A moredetailed system modeling is consequently needed before more specific conclusionscan be made.

In particular, a radio resource based admission control should be included(for both problems), and methods for estimating the operator specific load arerequired. In addition, it would be interesting to combine our algorithm with the

9Herein, an urban scenario with 20m correlation distance was assumed for the log-normalshadow fading. In, for example, suburban environments a larger separation of base stationswould be required to obtain such gains.


Inter−operator site distance 78m Inter−operator site distance 624m0

50

100

150

200Two receiving antennas and round robin scheduling


20

40

60

80

100

120Four receiving antennas and proportional fair scheduling

Gai

n in

10t

h pe

rcen

tile

user

thro

ughp

ut w

ith n

atio

nal r

oam

ing

[%]

1 user/cell3 users/cell10 users/cell


(a) Lower 10-percentile user throughput


10

20

30

40

50Two receiving antennas and round robin scheduling


5

10

15

20

25

30

Gai

n in

ave

rage

use

r th

roug

hput

with

nat

iona

l roa

min

g [%

]

Four receiving antennas and proportional fair scheduling



(b) Average user throughput

Figure 3.5: The relative gain with national roaming in 10-percentile userthroughput (upper graph) and average user throughput (lower graph). For bothmeasures, two different system configurations with different order of diversityagainst Rayleigh fading have been simulated.

3.5. Conclusions 53

elastic bit rate method proposed in [68] for the case of mixed circuit and packetswitched radio bearers. A theoretical analysis of the priority queuing modelpresented in Paper 5 would also be useful in order to validate the simulationresults and explain the behavior of the algorithm more consistently.

National roaming has herein been discussed in the context of cellular opera-tors. A major obstacle for that to materialize, however, is that coverage still is avery important competitive advantage for some operators. Yet, it is interestingto understand what the gains would be, and it should be noticed that nationalroaming also could be of interest for the emerging public WLAN operators andother wireless operators with fractional coverage only.

Chapter 4

Concluding Remarks

This chapter concludes the first part of the thesis with a summary of the pre-sented results and a few recommendations for future research in this field.

4.1 Summary

The main theme of this thesis has been cost efficient provisioning of mobile dataservices. We have focused on two methods of relevance for MNOs; adoptingnetwork deployment to local variations in traffic demand and network sharingbetween multiple operators. Both solutions are, as we see it, quite promisingand the key findings are summarized next.

Infrastructure Cost Modeling

We first assessed the costs of multi-access networks and compared these to con-ventional hierarchical cell structures (with a single radio access technology). Forthis purpose a network cost model, which accounts for the average cost andperformance of different BSs, was developed. The overall infrastructure cost iscalculated for a network composed of one or several radio access technologies.To model local variations in traffic, which are necessary to study the need forspecific “hot spot”-solutions, we generated statistical traffic maps according toa log-normally distributed, spatially correlated, stochastic variable. With a fewexamples, covering both existing and proposed future systems, we also illus-trated how the model can be used to estimate at what average traffic densitiesdifferent systems are most cost efficient.

With the modeling and assumptions used, multi-access networks consistingof WCDMA macro and micro-cells in combination with IEEE 802.11g WLANalways yield similar or lower infrastructure costs as compared to single-accessWCDMA networks with hierarchical cell structures. Hence, from this perspec-tive, WLAN should serve as a viable substitute for pico-cells in hot spots. How-

55

56 Chapter 4. Concluding Remarks

ever, even though both multi-access networks and hierarchical cell structuresmanage to lower the cost to some extent, the cost will always increase linearlywith traffic demand at some level (with a given set of technologies).

Using the elasticity of infrastructure cost as performance measure, we alsoillustrated at what average traffic densities the throughput, range, and costof different radio access technologies should be improved (when deployed in amulti-access network). This was exemplified with a network of WCDMA HSDPAmacro cells and IEEE 802.11g APs. For this case, we observed that throughput ismost important to increase for macro-cell BSs (at a maintained range), whereasincreasing range and reducing cost for sites and “last mile”-transmission have ahigher impact for WLAN.

As a comparison, we also evaluated both future cellular concepts (S3G, 4G,etc.), as well as open, user deployed, WLAN APs combined with a WCDMA HS-DPA macro-cell network. These results point at two key directions for expandingnetwork capacity if demand for mobile data services increase significantly. Thatis, an operator could either choose to increase capacity in their macro-cells at a(at least) maintained cell range, or resort to user deployed WLAN APs exploitingexisting fixed broadband networks for the “last mile”-transmission.

Multi-Operator Resource Sharing

Two issues related to network sharing between multiple operators have also beenaddressed. For this scenario, we studied how radio resources can be allocatedin a fair manner between operators sharing the same RAN. More specifically,an admission control of new connection requests (calls) with a non-preemptivepriority queuing based on operator specific load was proposed. As an initial step,using queuing system simulations, we showed that the method is promising forsystems with a large number of channels per cell. Hence, for cellular systems,this method should mainly be of interest for macro cell BSs serving many userssimultaneously. With a few users per cell only, other methods relying on capacityreservations or preemption of connected bearers have to be considered.

Finally, we estimated what gains in user throughput that can be expectedby allowing for roaming between overlapping cellular networks. This type ofnational roaming could, for instance, be utilized by MNOs in order to try out newservices without taking on large upfront investments. With a statistical systemmodel for uplink best effort data traffic, we showed that gains are significant forthe users that experience lowest throughput. Interesting to note is also that,thanks to increased diversity against shadow fading, a significant portion of thegain is present already with almost co-located BSs.

4.2 Future Work

Departing from these results, a number of interesting research problems arise.With the proposed network deployment model, a natural extension would be

4.2. Future Work 57

to estimate the empirical distribution of data rates that users experience withdifferent technology mixes. Refined system modeling and various sensitivityanalyses are also of great interest and importance for the cost evaluation. Inparticular, the spatial traffic modeling should be verified for mobile data services,and the effect of different user distributions should be investigated. If possible,local demand and pricing for higher user data rates should also be modeled.

From a techno-economical perspective, case studies with empirical data fromspecific markets and scenarios are also relevant extensions of this work. Ina broader sense, future research considering multi-access networks should alsoinclude marketing strategies, multi-mode terminal availability, legacy infrastruc-ture, cross-elasticity of demand between systems, etc. As this is a fundamentalpart of future wireless networks, it should be an important topic on the wirelessresearch agenda and in particular for techno-economical research.

For what concerns fair radio resource sharing between operators, more de-tailed system modeling is needed with respect to the characteristics of a cellularnetwork (with time-varying path gains, interference, etc.). Furthermore, the pro-posed priority queuing mechanism relies on operator specific load measurements.This could be difficult to measure in interference limited systems, and averagingover both time and multiple cells may therefore be beneficial. Moreover, giventhe rare event that more than two calls are queued in a specific cell, it could besufficient to have low and high priority calls only and this approach could alsobe investigated further, as well as analytical solutions of the operator specificblocking probability (with proper approximations).

In the assessment of data rates with national roaming, we did not considertrunking efficiency (which increases as a function of total system capacity). Asfurther work the capacity gain with national roaming at a given blocking prob-ability could be thus evaluated as a function of inter-operator site distance. Arelated aspect, which may seem attractive at a first glance but which we donot recommend for future work, is load balancing between cellular operatorsthat allow for national roaming. Firstly, the additional gain should be relativelysmall1. Secondly, as we elaborated on in [31], the administrative overhead be-tween cooperating operators has to be minimized.2 A more relevant technicalproblem would instead be how to design neighbor cell lists for handover mea-surements when multiple networks are accessible (to avoid exhaustive searches,which decrease measurement accuracy).

1In cellular systems, connecting to the BS with lowest path loss is most often sufficient.2Notice also that the trunking gain obtained by having access to more channels is present

also without advanced load balancing schemes, since it is sufficient if users can be redirectedto other operators at congestion.

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[71] Third Generation Partnership Project (3GPP). Shared network support inconnected mode (release 5). Technical Report TR3.012, Technical Specifi-cation Group Radio Access Network (TSG–RAN), September 2002. Version1.0.0.

[72] Nilo Casimiro Ericsson. Revenue – the criterion. In Proc. Personal Com-puting and Communication (PCC) Workshop, November 2003.

[73] Emmanuel Marilly, Olivier Martinot, Stephane Betge-Brezetx, and GerardDelegue. Requirements for service level agreement management. In Proc.IEEE Workshop on IP Operations and Management, pages 57–62, 2002.

[74] Telecommunication Management Forum. SLA Management Handbook,June 2001. TMF GB917.

[75] Ng Chee Hock. Queueing Modelling and Fundamentals. John Wiley & Sons,1996.

[76] Clayton M. Christensen. The Innovator’s Dilemma. Harvard BusinessSchool Press, 1997.

[77] Geoffrey A. Moore. Crossing the Chasm. HarperCollins, 3rd edition, 2002.

[78] COST 231. Digital mobile radio towards future generation system. Avail-able: http://www.lx.it.pt/cost231/, 1999. Final Report.

[79] European Telecommunication Standard Institute (ETSI). Selection proce-dures for the choice of radio transmission technologies of the UMTS (UMTS30.03). Technical Report TR 101 112, April 1998. Version 3.2.0.

[80] John G. Proakis. Digital Communications. McGraw-Hill, 4th edition, 2001.

[81] Eurescom. Investment analysis modelling (deliverable 2). Available:http://www.eurescom.de/, August 2000. Project P901-PF, Deliverable 2.

[82] Martha Amram and Nalin Kulatilaka. Real Options: Managing StrategicInvestment in an Uncertain World. Oxford University Press, December1998.

66 References

[83] Helmut Meier, Roman Friedrich, Hanno Blankenstein, and Alexan-der Froment-Curtil. How wireless carriers will make mobile data pay.Available:http://www.strategy-business.com/enewsarticle/enews102303,October 2003.

[84] Helmut Meier, Roman Friedrich, Hanno Blankenstein, and JonathanCorp. Mobile data: Creating sustainable value. Available:http://www.boozallen.de/content/downloads/mobile data.pdf, 2003.

[85] Costas Courcoubetis and Richard Weber. Pricing Communication Net-works: Economics, Technology and Modelling. John Wiley & Sons, 2003.

[86] Thomas T. Nagle and Reed K. Holden. The Strategy and Tactics of Pricing– A Guide To Profitable Decision making. Prentice Hall, 2nd edition, 1995.

[87] John Hagel III and Marc Singer. Unbundling the corporation. HarwardBusiness Review, 77(2):133–141, March/April 1999.

[88] Gunnar Almgren. Roaming between Wireless ISPs: Closed club or com-mons? In Proc. Stockholm Mobility Roundtable, May 2003.

Appendices

67

Appendix A

Cost Modeling and PricingStrategies

In this appendix we first describe and motivate the cost modeling used in theinfrastructure cost analysis, and further summarize a few basic pricing strategiesthat quite often are used by MNOs.

A.1 Discounted Cash Flow Modeling

It should be stressed that we deliberately have chosen a simplistic model for thisinitial assessment of heterogeneous wireless infrastructure costs. The interestedreader is further referred to [11] for standard micro-economical cost definitions,and [81] for an excellent overview of more detailed cost modelling applied totelecommunications.1

The purpose with our model is to include both investments and runningcosts.2 This can be done in several ways, where we have chosen a methodinspired by Net Present Value analysis (also referred to as Discounted CashFlow modeling) which is widely used for rudimentary investment analysis [11].We thus calculate the cost (per BS) in present value, with a conventional 10%discount rate.3

In general, the discount rate (r) reflects how willing a firm is to take risks.It is thus of great importance, since it directly will affect the total net presentvalue. For more explicit derivations of r, a model often used is the WeightedAverage Cost of Capital (WACC). This may be calculated as a function of thecost of debts (Cd), tax level (T ), cost of equity (Ce) and the size of debt (D)

1As noted in [81], cost modeling becomes quite complex when used for real investmentanalyses and regulation of inter-connection charges.

2Often denoted Capital Expenditures (CAPEX) and Operational Expenditures (OPEX).3Here, we could as well have annualized the investments, for example with a linear depre-

ciation during the system life cycle.

69

70 Appendix A. Cost Modeling and Pricing Strategies

and equity (E) according to:

r = Cd(1− T )D

D + E+ Ce

E

D + E(A.1)

As we can see from the model above the discount rate will depend on the ratiobetween equity and debt for the investment. Furthermore, the discount rate mayvery well vary as a function of time. A more detailed Net Present Value analysisthus requires detailed information on the investment to be judged. Anotherpopular method for more advanced investment analysis, which lends itself wellto the fast changing telecommunication industry [81], is the real options approach[82]. With this method, also future opportunities can be modeled explicitly.

A.2 Pricing Strategies

This appendix includes an overview of common pricing strategies according tomicro-economic theory [11], with a few examples of applications in mobile com-munications.

Pricing with Market Power

Microeconomic theory tells that a firm acting on a perfectly competitive market,where the price can not be affected by a single firm, should produce goods untilthe marginal cost equals the price [11]. This way the competitive firm maximizesits profit in the short run. For firms with market power, which is the interestingcase in practice, the level of output should be chosen in another way.

The simplest case is the monopolistic firm, where the output level should bechosen so that marginal revenue equals the marginal cost. The monopolist willhence set the price significantly higher than the competitive price.

In oligopoly markets, characterized by that a few firms produce most or allof the goods or services, some firms will earn substantial profits in the longrun. This is because there are market entry barriers that make it difficult orimpossible for other firms to enter. The price will be determined based onstrategic decisions, but the price should be set somewhere between the pricelevel chosen by a monopolistic and the price in a perfectly competitive market.

It is also important to understand that there are a number of ways in how afirm can increase its’ revenue (capturing parts of the consumer surplus) throughdifferent kind of price discriminations.

Price Discrimination

Price discrimination is often implemented by mobile operators, for instance bymarketing different subscriptions towards different subscriber groups (“blockpricing”). Operators thereby charge differently per, for example, minute of usedependent on the total number of call minutes per month.

A.2. Pricing Strategies 71

Another standard method is to brand and price the same product differently,and market different brands towards different consumer groups. This way, usersthat are willing to pay more can be attracted to the brand with higher price(even though the product in principle is the same). We can see examples of thisamong mobile operators who have launched own MVNOs under separate brands.The objective is to be able to target low-price segment without damaging themain brand.

Both inter-temporal price discriminations and peak-load pricing are verycommon in mobile networks. The aim with the inter-temporal price discrim-inations is to charge early adopters more than needed, since they have a highdemand for the product and consequently may be willing to pay more. Peak loadpricing is implemented in such a way that telephone calls cost more during officehours than at night time and during weekends. This is particularly importantif we take into account that mobile networks have to be dimensioned for peakload (during busy hour).

Value versus Volume Based Pricing

The principles of value and volume based pricing in communication networkshave been debated during the last years. Fixed broadband operators have fo-cused on attaining customers by charging “flat rate” pricing [83,84]. Some MNOshave also use the same strategy to acquire more voice subscribers and reducechurn, in particular (so far) in the United States. Flat rate pricing for GPRS and3G data services are normally only used by MNOs as a way of marketing newservices, and to teach their customers to use and appreciate the new services.

At the same time, volume based pricing should be avoided in mobile net-works [83]. In volume based pricing all costs, fixed and variable, are allocatedto the produced services proportional to the amount of bottleneck resource theyconsume [11]. At each given point in time a mobile network is limited by thenumber of bits available. Using this logic, each application should be priced pro-portionally to the number of transmitted bits [85]. However, this rapidly makescapacity demanding data services very expensive, especially since a user doestypically not experience any relation between data volumes and the perceivedvalue. Hence, volume based pricing is very problematic in mobile data networksand it will will fail to capture significant parts of the consumer surplus [11,83,86].To increase their revenues, MNOs may instead use value-based pricing, whereconsumers are charged per service rather than per bit (e.g. 1 Euro per song), assuggested in [83,84]. In practice, though, it is rare that only one pricing strategyis used. This is simply because pricing in practice is too complex and dependvarious controllable and uncontrollable variables [86].

Appendix B

Network Sharing Use Cases

Infrastructure sharing between multiple operators can implemented in severalways [1, 27,30,67]. Physical infrastructure, such as masts and antenna systems,have been shared quite frequently already in second generation systems. Sharingthe complete RAN and part of the core network has just recently been imple-mented in some countries during the roll-out of 3G networks. These networksharing methods are of course suitable for different use cases and, in summary,the following flavors of network sharing are commonly referred to in the litera-ture:

Site sharing Only non-intelligent equipment at base station sites are shared.For example masts and power supplies, possibly also antenna systems.

BS sharing The BSs (and “below”) are shared, but operators have their ownradio network controllers and core networks.

RAN sharing The whole RAN is shared, but core networks are still operatorspecific.

Common Shared Network Both the RAN and parts of the core network isshared between operators.

Geographical Sharing Operators agree to build and operate geographicallysplit networks, but allow for roaming for each others users.

MVNO Operators without own spectrum licenses and RANs, who offer mobileservices.

While the first five sharing solutions refer to network sharing between MNOs asa means to reduce cost in areas where the networks have significant overcapacity,the latter (MVNO) is driven by other factors which will be discussed next.

73

74 Appendix B. Network Sharing Use Cases

B.1 Mobile Virtual Network Operators

MVNOs have recently been identified by the national telecom regulators andcompetition authorities as a means to, similar to the unbundling of the local-loopfor DSL services, increase competition also in the oligopoly like mobile operatormarket. There are different types of MVNOs, with different background andcompetitive strategy. Most common today are

• branding MVNOs,

• fixed line telephony and broadband service providers, and

• mobile telephony operators targeting specific market segments.

In a wider perspective, the scope of the telecom operators’ business is since a fewdecades ago constantly undergoing a change [6]. Today, we see that new rolesare developed in the industry. Driven by the development towards a diverseportfolio of services, MNOs tend to focus more on developing and marketinguser applications and services. At the same time, telecom equipment vendorsseem to seize the opportunity to integrate upwards in the value chain and offernetwork operations and service platforms to the operators.

A separation between infrastructure, product innovation, and customer re-lationship businesses could often be beneficial from an organizational point ofview and stimulate innovation of new services [87]. However, this does not im-ply that MVNOs are best operated as small businesses. On the contrary, drivenby economies of scope and a strive to offer each customer as many services aspossible, customer relationship business tend to benefit from size [87].1. Hence,it is plausible that the role as service provider and intermediator for special-ized producers of content will be important both for MVNOs and MNOs in thefuture.

So far, however, with voice services as the key offering, the business caseof telecom MVNOs have relied on sufficiently low wholesale cost of networkaccess [27]. Even though regulators are well aware of that, and enforce costbased inter-connection charges, it is of course difficult for MVNOs to have a costadvantage towards the MNOs for the access.

Successful MVNOs therefore need other strategic advantages, such as a strongbrand, an existing customer base (that are interested in the service), a stream-lined customer care organization, or a niche service that the traditional MNOsneither could nor would offer [65].2 Of all these, the two most important driversfor MVNOs today, and during the foreseeable future, will therefore probably betheir opportunities to target niche services and customer segments [28,29,65,72].

1Infrastructure business such as access provisioning also benefit from size, but instead dueto economies of scale [87].

2For a win-win situation, the MVNO and hosting MNO should not compete for the samecustomers.

B.2. Inter-Operator Charging with Roaming Based Sharing 75

In this context, it should also be noted that, as emphasized in [30], thenetwork configuration need to be tailored for many mobile services (in terms ofQoS, area coverage, billing, etc.). Smaller MVNOs with little bargaining powerwill consequently need to adapt their service offering to the specific capabilitiesof the network. Hence, with only a few networks available, which most often areoptimized according to different criteria (depending on the business model of therespective MNO), and the strategic considerations outlined above, it may verywell be so that a specific MVNO do not have many viable options of networkproviders to choose between.

B.2 Inter-Operator Charging with Roaming

Based Sharing

As part of the GSM standard, international roaming has been extremely prof-itable for mobile operators, and a common assumption is that approximately15% of their revenues originate from roaming calls [88]. GSM operators todaysign bilateral roaming agreements with license holders, but typically not withMVNOs, from other countries. Hence, MVNO subscribers are referred to theroaming partners of their respective hosting network operator. Accounting be-tween operators is handled via international clearing houses. As we are usedto, subscribers normally pay for the international part of the call. The homeoperator, however, may have a discount with the visited operator and is thencharged less per voice minute than what see on the bill [88]. The core networkfunctionalities originally intended for billing, service provisioning, and mobilitymanagement with international roaming are naturally applicable also for roam-ing based sharing.

Part II

Paper Reprints

77

Chapter 5

Relation Between BaseStation Characteristics andCost Structure in CellularSystems (Paper 1)

Klas Johansson, Anders Furuskar, Peter Karlsson, and Jens Zander,In Proc. IEEE PIMRC 2004, October 2004.

79

81

RELATION BETWEEN BASE STATION CHARACTERISTICS AND COST STRUCTURE IN

CELLULAR SY STEM S

Klas Johansson1, A nders F u r u sk ar2, P eter Karlsson3, and Jens Z ander1

1 W ireless@ KT H , E lec tr u m 4 1 8 , S -1 6 4 4 0 Kista, S weden, {k lasj, jens.z ander}@ radio.k th.se2 E r ic sson A B , S E -1 6 4 8 0 S toc k holm , S weden, anders.f u r u sk ar@ eric sson.c om

3 T eliaS onera S weden, B ox 9 4 , S E -2 0 1 2 0 M alm o, S weden, P eter.C .Karlsson@ teliasonera.c om

Ab s tr a c t - A sim p le m ethod for estim ating the c osts of

b u ilding and op erating a c ellu lar m ob ile network is p ro-

p osed. U sing em p ir ic al data from a third g eneration m ob ile

sy stem ( W C D M A ) it is shown that the c ost is driv en b y

different fac tors dep ending on the c harac teristic s of the b ase

stations dep loy ed. W hen site density inc rease, op erational

and transm ission c osts tend to dom inate rather than radio

eq u ip m ent and site c osts. T he resu lts also show how, for

different c ap ac ity req u irem ents, the c osts c an b e m inim iz ed

b y a p rop er selec tion of for ex am p le m ac ro, m ic ro and p ic o

b ase stations. In m any sc enarios the m ac ro b ase stations y ield

the lowest c ost, indic ating that c ov erag e (c ell rang e) is an

im p ortant p aram eter when desig ning wireless sy stem s.

K e y w o r d s - T ele-ec onom ic s, c ost m odel, infrastr u c tu re

c ost, b ase station c ost

I . I N T R O D U C T I O N

T he c osts of p rov iding wide-area c ov erag e for hig h data

rate wireless ac c ess hav e b een disc u ssed widely in the

telec om indu str y ov er the last c ou p le of y ears. W hile m ob ile

op erators hav e str u g g led with hig h lic ense fees and roll-

ou t c osts du e to reg u latory req u irem ents for third g eneration

network s, tec hnolog ies su c h as W ireless L A N hav e ev olv ed

as c om p lem enting and in sp ec ifi c sc enarios ev en c om p et-

itiv e alternativ es [2 ] . T his has ev idently c ontr ib u ted to an

inc reased c ost awareness am ong b oth m ob ile op erators and

eq u ip m ent v endors [4 ] .

M ob ile infrastr u c tu re c ost was u nder stu dy already du r ing

the dev elop m ent of G S M and other 2 G sy stem s, howev er it

has b een m ore foc u sed only rec ently for 3 G and b ey ond. In

[6 ] the c osts of p rov iding m ob ile data serv ic es was analy z ed

in term s of ec onom ies of sc ale and sc op e. F u r therm ore,

c ost effec tiv e way s of c onfi g u r ing c ellu lar network s was

addressed in [1 ] and an em p ir ic ally b ased c ost m odel for

c ellu lar sy stem s was p rop osed in [7 ] .

I t is c om m only k nown that the c ap ac ity is p rop ortionally

to the b ase station density for a g iv en c ellu lar sy stem .

U nfortu nately , also the infrastr u c tu re c osts seem s to inc rease

alm ost linearly with the c ap ac ity req u ired (indic ating a low

deg ree of ec onom ies of sc ale). T his was disc u ssed in [9 ] ,

where the c ost str u c tu re of wireless ac c ess infrastr u c tu re

was analy z ed. I t was c onc lu ded that the network c ost r ises

linearly with the data rate p er u ser. T his shou ld hold for a

g iv en freq u enc y alloc ation p rov ided that the sam e c ov erag e

is req u ired, and was identifi ed in [1 0 ] as a k ey p rob lem for

p rov iding wideb and data serv ic es in wireless sy stem s.

A sim p le infrastr u c tu re c ost m odel was also p resented

in [9 ] (and dev elop ed fu r ther in [1 0 ] ) , in whic h the total

infrastr u c tu re c ost of a wireless sy stem is m odeled as linearly

p rop ortional to the nu m b er of b ase stations:

Csystem = cNb s, ( 1 )

where Nb s is the nu m b er of b ase stations and c is a c onstant

c orresp onding to the c ost p er b ase station. N ote that in [9 ]

c is assu m ed to b e the sam e for all b ase stations and it is

indep endent of the b ase station c harac teristic s.

H owev er, in a p rac tic al sy stem , a nu m b er of different

b ase station ty p es c ou ld b e u sed for different dep loy m ent

sc enarios and the req u irem ents on, e.g ., c ell rang e and relia-

b ility g reatly affec ts the total c ost p er b ase station (inc lu ding

c ap ital and op erational ex p enditu res). A s a c onseq u enc e the

c ost str u c tu re of a radio ac c ess network is dep endent of the

sy stem c onfi g u ration, i.e. the q u antities and ty p es of different

ac c ess p oints em p loy ed to ac hiev e v ariou s total network

c ap ac ities and c ov erag e.

T his top ic will b e treated fu r ther in the seq u el of this

p ap er, whic h is ou tlined as follows. T he ov erall distr ib u tion

of c osts in c u r rent m ob ile network s is p resented in S ec tion II .

T his disc u ssion ju stifi es a sim p le infrastr u c tu re c ost m odel

whic h is desc r ib ed in S ec tion II I . U sing this, the total c ost

and c ost str u c tu re is c alc u lated in S ec tion IV for different

dem ands and p otential solu tions for deliv er ing affordab le

wireless serv ic es with hig h data rates is disc u ssed b r iefl y .

T he p ap er is c onc lu ded in S ec tion V .

I I . O V E R A L L C O S T S T R U C T U R E F O R M O B I L E O P E R A T O R S

B efore g oing into a m ore detailed analy sis, let u s fi rst

look b r iefl y at the ov erall c ost str u c tu re of a m ob ile op -

erator as of today . T y p ic ally , their inv estm ents relate to

radio and transm ission eq u ip m ent, lic ense fees, site b u ild-

ou ts and installation of eq u ip m ent. T he ru nning c osts, in

tu rn, c onsider m ainly transm ission, site rentals, m ar k eting ,

ter m inal su b sidies, and op eration and m aintenanc e (O & M ) .

T he ex ac t b reak down of those c osts is of c ou rse c ase

sp ec ifi c , and it m ay v ar y sig nifi c antly b etween different

82 Chapter 5. Base Station Characteristics (Paper 1)

countries and operators. An attempt to model the costs and

rev enues for w estern E uropean operators w as done w ithin

the T O N I C project [ 5 ] . T he cost structure w as estimated

for operators prov iding U M T S serv ices in (i) a small and

sparsely populated country and (ii) a larg e country w ith

denser population.

F rom these results it is clear that cumulated running costs

dominate the total cost structure of a mob ile operator and,

according to [5 ] , they correspond to roug hly 7 5 % of the total

costs for a larg e country. M ore specifi cally, the running costs

are dominated b y non-technical costs, such as mark eting ,

terminal sub sidies and w ag es w hich can b e seen in T ab le 1 .

N ote also that transmission constitutes a sig nifi cantly hig her

portion of the running costs in the small country. T his since

’last mile’-transmission is priced per k ilometer in the model

in [5 ] and the distance b etw een b ase stations is hig her in the

small country ex ample, due to its low er population density.

T ab le 1

T ypical running cost structure (b ased on [5 ] ) .

Large country Small country

S ite rental 5 % 5 %T ransmission 5 % 2 5 %T erminal sub sidies 1 5 % 1 5 %M ark eting 2 5 % 1 5 %E mployees 5 0 % 4 0 %

Another interesting , althoug h perhaps not surprising , fact

is that the v ast part of the infrastructure related costs stems

from the radio access netw ork (including radio netw ork

controllers, b ase stations, sites, and ’last-mile’-transmission).

C ore netw ork eq uipment such as b ack b one transmission,

sw itches, routers, charg ing functionality, and sub scrib er reg -

isters only contrib ute to 1 0 - 3 0 % of the ov erall netw ork costs.

T his is in particular clear from the inv estment cost structure

presented in T ab le 2 .

T ab le 2

O perator inv estment structure (b ased on [5 ] ) .

Large country Small country

C ore netw ork 3 0 % 1 0 %S ite b uildout 3 0 % 5 0 %R adio access netw ork 4 0 % 4 0 %

I I I . C O S T E S T I M AT I O N M E T H O D AN D AS S U M P T I O N S

After the discussion ab ov e, it seems reasonab le to limit

the infrastructure cost model to the radio access netw ork .

T hus, only b ase station eq uipment, site costs and ’last mile’-

transmission are included and w e use a W C D M A system as

a case study. T he same cost model as g iv en in (1 ) can then

b e used, b ut it remains to determine the total cost per b ase

station c. H ow ev er, to mak e the model more realistic w e

need to address that an operator seldom has the same b ase

stations for ev ery scenario.

A. Infrastructure cost model

In principle there is an infi nite numb er of possib le con-

fi g urations of b ase stations (including different alternativ es

for sites, transmission, etc.) . A roug h div ision, thoug h, could

b e to stick to three main categ ories b ased on the cell rang e;

namely macro, micro and pico cell b ase stations. T he cost per

b ase station c should also b e sig nifi cantly different for those

b ase stations. F or ex ample, a small micro or pico b ase station

implies a low cost for eq uipment, site leases and installation

w hereas a larg e macro b ase station costs much more in those

aspects. O n the other hand, fi x ed costs not directly related

to the capacity of the b ase station are div ided b etw een many

users in a macro b ase station so the cost per user may still

b e low er in many scenarios.

T he total infrastructure cost for a mob ile operator could

then b e modeled as

Csystem = c1Nma c r o + c2Nmic r o + c3Np ic o , ( 2 )

w here c1, c2, and c3 are the total costs for macro, micro and

pico b ase station respectiv ely. T ypically c1 > c2 > c3 and, if

w e in the same w ay defi ne the max imum cell radius R per

b ase station, R1 > R2 > R3. H ence, different b ase stations

w ill minimiz e cost for different scenarios. H ow ev er, for the

sak e of simplicity, w e w ill study the different b ase stations

separately b ut k eep in mind that each cellular netw ork in

reality consist of a mix of b ase stations.

B . N etw ork dimensioning

T he numb er of b ase stations req uired, Nb s, is calculated

as a function of the demand specifi ed b y the:

• S erv ice area to b e cov ered, Aser v ic e.

• Av erag e capacity per user during b usy hour, Wu ser .

• N umb er of sub scrib ers w ithin the cov erag e area, Nu ser .

F urthermore, the dimensioning w ill b e done for dow nlink

only. T his should b e reasonab le since the dow nlink g enerally

limits the ag g reg ate capacity in a W C D M A system, w hile

the uplink limits the data rate per link and cov erag e w hen

the traffi c load is low [ 3 ] .

O nly a sing le carrier is assumed, to mak e the comparison

b etw een b ase station types simple (more carriers w ould

reduce the numb er of sites in a capacity limited scenario).

E ach b ase station has a g iv en max imum cell rang e (Rma x ) ,

minimum cell rang e (Rmin ) and supported capacity (Wma x ) .

F or simplicity the capacity is k ept constant, and does not

v ary as a function of the actual cell rang e (Rb s) . E ach cell has

circular cov erag e area, w hich according to [7 ] is a reasonab le

assumption.

T he netw ork can either b e cov erag e or capacity (interfer-

ence) limited and the numb er of b ase stations req uired is

dimensioned according to the follow ing model:

Nb s = m a x

{

Aser v ic e

π R2ma x

,Nu ser Wu ser

Wma x

}

, ( 3 )

83

assuming a continuous service area and that users are

uniformly distrib uted (as in e.g. [ 9 ] ) . N ote also that the

netw ork is dimensioned in an average sense, and that the

w anted b lock ing and outage p rob ab ility has to b e p ossib le

to achieve at the assumed cell range Rmax and b ase station

cap acity Wmax for each considered service. N ow , given that

the resulting cell range

Rb s =

√

As e r v ic e

π N b s

≥ Rmin , ( 4 )

the cap acity req uirements can b e met w ith the selected ty p e

of b ase station.

C. Empirical cost and performance data

T he p erformance and cost data related to the b ase stations

are given in T ab le 3 . A ll values are ap p rox imate, b ut should

b e rep resentative in a relative sense for a ty p ical W C D M A

sy stem dep loy ed during 2 0 0 3 . F or simp licity only an urb an

scenario is considered and ty p ical cell ranges and cap acities

are b ased on general estimates p rovided in [3 ] and do

not rep resent the p erformance of any sp ecifi c p roduct. W e

assume that the cap acity p er cell is higher for micro and

p ico cell b ase stations. T his since it is p ossib le to minimiz e

inter-cell interference b y a p rop er p lacement of the antennas

(b elow roof-top or indoors).

T he eq uip ment costs estimates have b een p rovided b y the

G artner G roup and the other cost p arameters are b ased on

[5 ] . T he macro b ase station is naturally much more ex p ensive

than the smaller b ase stations, b ecause of its higher outp ut

p ow er and cap acity , b ut also due to that it has to b e more

reliab le since more users are served p er b ase station. T his

clearly affects the costs for sites, installation and O & M .

T ab le 3

B ase station p erformance and costs

Macro BS Micro BS Pico BS

P erformance [ 3 ] :

S ectors 3 1 1C arriers (2 * 5 M hz ) 1 1 1M ax imum cell range (Rmax) 1 k m 0 .2 5 k m 0 .1 k mM inimum cell range (Rmin ) 0 .2 5 k m 0 .1 k m 0 .0 2 5 k mC ap acity (Wmax) 2 .2 5 M b p s 1 .2 5 M b p s 1 .7 5 M b p sInitial costs:

E q uip ment (S ource: G artner) 5 0 k e 2 0 k e 5 k eS ite b uildout [5 ] 7 0 k e - -S ite installation [5 ] 3 0 k e 1 5 k e 3 k eA nnu al costs [ 5 ] :

A nnual O & M 3 k e 1 k e 1 k eS ite lease 1 0 k e 3 k e 1 k eT ransmission 5 k e 5 k e 5 k e

’ L ast mile’-transmission costs are also included in the

model, and for this p urp ose w e use a simp lifi ed modeling

of leased lines. E ach b ase station is assumed to have the

same cost for transmission of 5 k e p er y ear. T he transmission

p rices are sub ject to a y early p rice erosion of 5 % . T he other

annual costs are assumed to b e constant.

D . D iscou nted cash fl ow model

T he total cost p er b ase station c is calculated in p resent

value using a standard economical method for cumulated

discounted cash fl ow s. B y doing so, w e can account for b oth

investments and running cost in the comp arison and analy z e

the total cost structure of different b ase stations.

T his is simp ly done b y summing up the discounted annual

cash fl ow s (in this case y early ex p enditures) for the w hole

netw ork life cy cle (K y ears) according to

c =

K−1∑

k= 0

ck

(1 + d)k, ( 5 )

w here d is a discount rate w hich is assumed to b e eq ual to

1 0 % . T he netw ork is in all ex amp les in the seq uel assumed

to b e used during K = 1 0 y ears and the cost for eq uip ment

and site b uildouts are accounted for in the fi rst y ear (k = 0 ) .

T hat is, the w hole netw ork is dep loy ed during the fi rst y ear.

E. D iscu ssion on th e model’s applicab ility

A lthough this is a very simp lifi ed model, w e b elieve

this ap p roach can b e useful to understand the fundamental

characteristics of different technical solutions. E .g., w hen

different b ase stations are ap p licab le and w hat the b ottle-

neck s are in today ’s sy stem. Y et, a few imp ortant things

that distinguish the model from a real netw ork could b e

w orth to p oint out. F irstly , neither b ase stations, nor users,

are uniformly distrib uted as w e assume in the simp le model.

S econd, a netw ork ty p ically consists of a variety of b ase

stations, transmission and antenna sy stem designs.

T he latter is p artly due to that an op erator naturally

op timiz es the choice of technology dep ending on the sp e-

cifi c dep loy ment case as discussed ab ove. B ut also b ecause

netw ork s evolve over time, and ex isting infrastructure can

q uite often b e reused w hen new technology is rolled out.

H ence, the p revious investments in, e.g., sites and cab ling

are treated as long term investments and do not add to the

incremental cost of adding more cap acity . N ote also that

the emp irical cost and p erformance data only should b e

considered as estimates, and the statistical signifi cance of

those is not k now n. H ow ever, w e b elieve that the fi gures

used refl ects the costs for a ty p ical mob ile netw ork fairly

w ell and the conclusions should therefore hold also for other

cellular technologies, such as G S M or C D M A 2 0 0 0 .

I V . R E S U L T S

In this section w e w ill illustrate how the total sy stem cost

varies as a function of demand, given b y the user density

and demanded b usy hour throughp ut p er user Wu s e r , and the

b ase station ty p e (macro, micro and p ico). T his is follow ed

b y an analy sis of the cost structure of the b ase stations under

study , and a b rief discussion on technical imp rovements

req uired to p rovide high data rates w ith w ide area coverage.

84 Chapter 5. Base Station Characteristics (Paper 1)

102

103

104

105

101

102

103

104

U s e rs /k m 2

Infra

stru

ctur

e co

st p

er k

m2 in

pre

sent

val

ue [k

Eur

o]

A v e ra g e b u s y h o u r th ro u g h p u t p e r u s e r Wu s e r

= 1k b p s

C o v e ra g e lim ite d

P ic o B S

M a c ro B S C a p a c ity lim ite d

M ic ro B S

Fig. 1. Infrastructure cost for different base stations.

A. Infrastructure cost

Fig. 1 illustrates th e total infrastructure cost Csystem for

different base stations w ith a data rate Wu ser = 1k bp s. T h is

could, e.g., corresp ond to a sp eech serv ice of 10 k bp s at

0 .1E rlang traffi c load (ty p ical corp orate user). A ccording to

(3 ) th e infrastructure cost is constant as long as th e sy stem

is cov erage lim ited. T h en, as m ore base stations are needed

to m eet th e cap acity req uirem ents th e total cost increase

linearly according to th e cost p er base station c.

From th is p icture it is p ossible to fi nd th e base station

ty p e th at m inim iz e infrastructure cost for different user

densities. In th is sp ecifi c case, w ith Wu ser = 1k bp s, th e

m acro cell base stations sh ould be used until dem and ex ceed

4 0 0 0 users/k m 2, th ereafter m icro cell base stations could be

w orth w h ile to introduce up to a v ery h igh user density (ap -

p rox im ately 2 0 0 0 0 users/k m 2) w h en p ico cells are ch eap est.

W e can also see w h en dem and increase signifi cantly so

th at a denser dep loy m ent is needed, th e cost p er user is

low ered and th ere is a certain degree of scale econom ics

as dep icted in Fig. 2 . H ere w e h av e p lotted th e cost p er user

as function of user density for Wu ser eq ual to

• 0 .2 k bp s (ty p ical p riv ate sp eech user at 2 0 m E rl) ,

• 1k bp s (ty p ical corp orate sp eech user at 10 0 m E rl) , and

• 10 k bp s (data user dow nloading ap p r. 5 M B /h our)

resp ectiv ely . N ote also th at th e cost p er user dim inish es

step w ise because of th e lim ited num ber of base station ty p es

used in th is ex am p le and th at each base station is cov erage

lim ited w ith in som e region.

B . C ost structure

T h e cost structure of different solutions is h ere div ided in

th ree p arts:

• R adio: B ase station eq uip m ent and discounted O & M

costs.

101 102 103 104 10510

−2

10−1

100

101

U s e rs /k m 2C

ost p

er u

ser i

n pr

esen

t val

ue [k

Eur

o]

Wu s e r

= 10k b p s

Wu s e r

= 1k b p s

Wu s e r

= 0.2k b p s

Fig. 2 . M inim um cost p er user for different cap acity .

• S ites: S ite buildout & installation and discounted site

leases.

• T ransm ission: discounted lease line costs.

T h e total cost p er base station c and th e resp ectiv e cost

structure are giv en in T able 4 . T h e v alues are based on th e

assum p tions giv en in S ection III.

T able 4

C ost structure w ith different base stations.

Base station R ad io S ites T r ansm ission C ost p er BS

M acro 7 0 k e 16 8 k e 2 8 k e 2 6 6 eM icro 2 7 k e 3 5 k e 2 8 k e 9 0 k eP ico 12 k e 10 k e 2 8 k e 5 0 k e

In th is ex am p le m icro base stations are 6 6 % ch eap er th an

m acro base stations, w h ereas th e cost for a p ico base station

is only 4 4 % low er th an a a m icro base station. T h is h as

a rath er intuitiv e ex p lanation: th e eq uip m ent cost is low er

and site costs can be reduced signifi cantly as th e req uired

cell range decreases. H ow ev er, th e transm ission costs are th e

sam e for all base stations in th is ex am p le. H ence, as th e base

station range decrease th e cost is driv en by transm ission,

rath er th an by radio and site costs.

C . P otential d ev elop m ent p ath s tow ard s h ig h er d ata rates at

a low cost

A k ey p roblem for deliv ering data serv ices in w ireless

sy stem s is th e cost p er bit, w h ich does not decrease in

th e sam e p ace as dem and increases w ith today ’s cellular

tech nology . A s discussed in [9 ] w e can not assum e th at th e

users’ total w illingness to p ay for w ireless serv ices increase

signifi cantly in th e future (at least not in th e sam e order as

data traffi c is ex p ected to grow ) . T h erefore, nov el solutions

seem to be needed in order to ach iev e greater econom ics of

85

scale in wireless networks. But how can this be achieved in

p ractice?

T here are m ainly two p ossible develop m ent tracks. O ne is

to re-use ex isting sites, or even reduce the num ber of sites,

by ex tending the cap acity of ex isting solutions. T his can in

p rincip le be done by either

1 ) allocating m ore sp ectrum for third g eneration net-

works,

2 ) increase sp ectral effi ciency , e.g . by utiliz ing adap tive

antennas.

3 ) introduce m ulti-hop technolog y in the cellular net-

works,

or using a com bination of those.

Another way would be to actually allow for denser de-

p loy m ent by decreasing the cost p er base station. U sing the

cost structure analy sis above as a starting p oint, there is a p o-

tential in lowering transm ission and O & M costs for p ico cell

base stations. Instead of the ex p ensive leased lines (E 1 /T 1 )

or m icrowave radio links cheap er transm ission technolog ies

could be introduced, e.g ., wireless fi x ed broadband or x D S L .

T he costs for O & M and sites could be reduced by allowing

for p rivately owned and dep loy ed base stations, p ossibly

connected to ex isting fi x ed broadband or local area networks.

S im ilar to W ireless L AN access p oints, one could im ag ine

sm all 3 G base stations owned by individuals or enterp rises.

H owever, for such solutions to be econom ically feasible,

som e sig nifi cant m odifi cations are req uired also in the core

network eq uip m ent (sim p ly to handle a larg e increase in the

num ber of base stations). It also req uires slig htly m odifi ed

business m odels and value chain constellations for the m o-

bile op erators.

V . C O N C L U S I O N

A sim p le m odel for estim ating the infrastructure costs

of cellular sy stem s was p rop osed. T he m odel is based on

averag e cost and p erform ance data from third g eneration

m obile networks and includes both investm ents and running

costs. W ith this m odel, it is p ossible to analy z e the infrastruc-

ture cost as a function of dem and for different base station

confi g urations.

T he cost drivers were shown to be a function of the

characteristics of the base stations. W ith m acro base stations

the costs m ainly considers base station eq uip m ent, O & M

and sites, whereas for p ico-cell dep loy m ent the ’last m ile’

transm ission dom inates with the p resent technolog y . Results

also show that the m acro base stations y ield the lowest cost

in m any scenarios. T his indicates that coverag e (cell rang e)

is an im p ortant p aram eter when desig ning future wireless

access sy stem s.

F urther studies in this area could include im p roved m od-

eling and m ethods for evaluating the econom ical g ain of

new technical features, e.g . by m eans of elasticity analy sis.

Also the p otential econom ical benefi ts and suitable business

m odels for a wide dep loy m ent of p ico base stations, or other

ty p es of local access p oints, could be interesting to study in

m ore detail.

AC K N O W L E D G M E N T

T hanks to M r. J ason C hap m an (T he G artner G roup ) and

D r. J an W erding for p roviding em p irical cost data and valu-

able insig hts on the fi nancial asp ects of wireless networks.

T he fi nancial sup p ort from the S wedish F oundation for

S trateg ic Research via the Affordable W ireless S ervices and

Infrastructure (AW S I) p rog ram is g reatly ap p reciated.

RE F E RE N C E S

[ 1 ] B. G avish and S . S ridhar, “ E conom ic asp ects of con-

fi g uring cellular networks” , Wireless Networks, V ol. 1 ,

N o. 1 , F eb. 1 9 9 5 , p p .1 1 5 - 1 2 8

[ 2 ] J . H arno, “ 3 G Business C ase S uccessfulness within

the C onstraints S et by C om p etition, Reg ulation and

Alternative T echnolog ies” , in the P roc eed in g s of th e

F I T C E E u rop ea n T elec om m u n ic a tion s C on g ress, 2 0 0 2 .

[ 3 ] H . H olm a and A. T oskala, “ WC D M A for U M T S ” , J ohn

W iley & S ons, 2 0 0 2 .

[ 4 ] D . K atsianis et al., “ T he econom ic p ersp ective of the

m obile networks in E urop e” , I E E E P erson a l C om m u n i-

c a tion s M a g a z in e, V ol. 8 , N o. 6 , p p 5 8 - 6 4 , D ec. 2 0 0 1 .

[ 5 ] F . L oiz illon et al., “ F in a l resu lts on sea m less m ob ile IP

serv ic e p rov ision ec on om ic s” , I S T - 2 0 0 0 - 2 5 1 7 2 T O N I C

D eliverable num ber 1 1 , O ct. 2 0 0 2 .

[ 6 ] D .P . Reed, “ T he C ost S tructure of P ersonal C om m u-

nication S ervices” , I E E E C om m u n ic a tion s M a g a z in e,

V ol. 7 , N o. 2 , Ap r. 1 9 9 3 , p p . 1 7 3 - 1 8 5 .

[ 7 ] R. S tanley , “A m ethodolog y for evaluating and op tim iz -

ing wireless sy stem infrastructure costs” , in P roc eed -

in g s of th e I E E E I n tern a tion a l S y m p osiu m of P erson a l,

I n d oor a n d M ob ile R a d io C om m u n ic a tion s ( P I M R C ) ,

1 9 9 6 .

[ 8 ] F .J . V elez , L .M . C orreia, “ C ost/revenue op tim isation in

m ulti-service m obile broadband sy stem s” , in the P ro-

c eed in g s of th e I E E E c on feren c e on P erson a l, I n d oor

a n d M ob ile R a d io C om m u n ic a tion s ( P I M R C ) , 2 0 0 2 .

[ 9 ] J . Z ander, “ O n the cost structure of future wideband

wireless access” , in p roc eed in g s of th e I E E E V eh ic u la r

T ec h n olog y C on fereren c e (V T C ) , 1 9 9 7 .

[ 1 0 ] J . Z ander, “Affordable m ultiservice wireless networks

- research challeng es for the nex t decade” , in P roc eed -

in g s of th e I E E E I n tern a tion a l S y m p osiu m on P erson a l,

I n d oor a n d M ob ile R a d io C om m u n ic a tion s ( P I M R C ) ,

2 0 0 2 .

Chapter 6

An Infrastructure CostEvaluation of Single- andMulti-Access Networks withHeterogeneous TrafficDensity (Paper 2)

Anders Furuskar, Klas Johansson, and Magnus Almgren,In Proc. IEEE VTC2005 Spring, May 2005.

87

89

An Infrastructure Cost Evaluation of

Single- and Multi-Access Networks with

Heterogeneous Traffic Density

Anders Furuskär and Magnus Almgren

W ireless Access Networks

Ericsson Research

Kista, Sweden

[anders.furuskar, magnus.almgren]@ ericsson.com

Klas Johansson

W ireless@ KTH,

The Royal Institute of Technology

Electrum 418, S-164 40 Kista, Sweden

Email: klasj@ radio.kth.se

Abstract � Traditional performance measures like capacity, cell

radius and supported QoS are often insufficient when comparing

wireless networks with different network architectures and cost

structures. Instead, in this paper, infrastructure cost is used to

compare different operator deployed single- and multi-access

wireless networks, including 3G, W LAN and proposed 4G radio

access technologies. For this purpose a model for the geographi-

cal distribution of traffic is introduced. Despite the spatially non-

uniform traffic demand, single-access solutions like W CDM A

High-Speed Downlink Packet Access (HSDPA) or Long-Term 3G

Evolved, with high capacity macro cellular base stations, typi-

cally yield the lowest costs per user. In particular this holds for a

hypothetical Long-Term 3G Evolved system operating in

450M Hz spectrum, which indicates the importance of good cov-

erage. Operator deployed W LAN-only solutions are more expen-

sive even for small fractions of supported users. M ulti-access

solutions, combining for example W CDM A DCH or HSDPA with

W LAN, do not seem to provide better cost efficiency than stan-

dard hierarchical cell structures in single-access systems. Instead,

multi-access solutions have to be motivated by other factors like

peak data rates and spectrum availability.

Keywords: Infrastructure cost, Tele-economics, multi-access,

WCDMA, 3G, Long-Term 3G Evolution, 4G, WLAN

I. INTRODUCTION

Mobile network operators are typically interested in maxi-mizing the profit determined by the revenue generated by their systems and their costs. Traditional performance measures used for single-access cellular systems, such as coverage and capac-ity, are effective measures of the relative improvements for specific systems. Considering also deployment aspects and that different systems typically have different cost structure, techni-cal measures, like spectral efficiency, are however, as dis-cussed in e.g. [1], insufficient to compare different systems. Ideally both costs and revenues should be included in the analysis, as in [2], and availability of spectrum, previous assets, and other strategic issues need to be taken into account. Due to difficulties in, e.g., predicting end users willingness to pay, this is quite complex. A simpler initial step, for relative compari-sons only, is to compare the system cost for equal potential revenues (to simplify; number of supported users) and this is also the focus of this paper.

More specifically, the radio access network infrastructure cost is normalized per user and compared between different single- and multi-access system concepts as a function of traf-fic intensity per user and relative user coverage. The system concepts compared include the cellular systems W CDMA DCH, W CDMA HSDPA, and preliminary Long-Term 3G Evolution and 4G proposals, as well as the W LAN system con-cepts IEEE 802.11b, a and g. Also multi-access combinations of these, an expected characteristic of future wireless networks [3], are included. To evaluate the benefits of the multi-access networks, a heterogeneous traffic density model is applied.

Recently a number of wireless network infrastructure cost analyses for single-access networks have been presented, e.g. [1] and [2]. These conclude that the infrastructure cost, includ-ing both capital expenditures (CAPEX) and operational expen-ditures (OPEX), is largely proportional to the number of access points deployed. Equivalent cost figures per access point, are also presented, which can be used to simply assess the total infrastructure cost for a given deployment. Models of the spa-tial distribution of mobile users have been presented in e.g. [4] and [5]. This paper combines the above results and extends the scope to cover multi-access networks.

In what follows, Section II briefly discusses the impact of the spatial traffic distribution on the cost efficiency of different single- and multi-access system concepts, and presents the de-ployment principles used in this study. An overview of the ra-dio network, user behavior, and economical models and as-sumptions is given in Section III. Numerical results are pre-sented in Section IV, followed by conclusions in Section V.

II. DEPLOYMENT ASPECTS

In scenarios with homogeneous, uniform traffic densities, single-access solutions with only one type of access point man-age to maximize cost efficiency. For example, with a high area traffic demand micro cellular base stations may be most cost efficient, whereas macro base stations typically yield the lowest cost in areas with less traffic per area unit. Solutions with a mix of macro, micro and pico cells, as well as multi-access con-cepts, may be expected to be more cost efficient than single access networks only in scenarios with heterogeneous traffic densities. Yet, this is not a sufficient condition. It is also re-quired that traffic peaks (hotspots) are very few and strong.

90 Chapter 6. Heterogeneous Infrastructure Cost (Paper 2)

This is explained by the following example based on macro and micro cellular base stations.

Assume that a single-carrier macro cell layer is deployed for fundamental coverage. As depicted in the first example in Figure 1, the capacity of this network is sufficient to serve traf-fic density patterns for which the average is within the macro cell area capacity. Local areas with traffic demands exceeding the area capacity are supported. Then, if the average traffic density exceeds the total area capacity of the first macro car-rier, either a second macro carrier, additional macro base sta-tions or micro cells can be deployed. W hat solution that brings the lowest cost depends on the statistical distribution of traffic. In the second example, traffic is high and strongly varying with many relatively small peaks. Then adding a carrier to the macro-cellular layer, or deploying macro base stations more densely, is typically most efficient. Deploying a micro cell in each of the many local traffic density peaks would require a large amount of micro sites, and hence be costly, since each micro base station would have excess capacity and be poorly utilized. In situations like the one in example three, however, micro cells are motivated. Here, the peaks in traffic density are very strong and rather few, so that only a few micro cells need to be deployed, and the cost for this is lower than that of ex-tending the macro layer.

A heterogeneous traffic density alone is thus not sufficient for motivating micro and pico cell, or multi-access solutions from an infrastructure cost perspective. There are also require-ments on (i) a high overall traffic density, (ii) strong variations in traffic density, and (iii) special spatial correlation properties.

A. Deployment Principle

The deployment principle used in this study is to first deploy macro cells for full area coverage, and then complement with micro or pico cells, or W LAN access points, where it is needed for capacity reasons.

In more detail, first, the system area Asys is divided into Ndifferent 40x40m elements. The (center) position and the traffic generated in element n are denoted Pn and TEn respectively. For each RAT r, candidate access point sites are positioned on a regular hexagonal grid, with site-to-site distances according to the AP cell radii. The position of AP m is denoted Sm. Based on the site positions, an association between sites and traffic elements is made, so that the elements in the set S

rm belong to

AP m. The association is done so that traffic elements are asso-ciated with the closest site:

{ }{ }mPAPdnS nkkrm == ),(minarg (1)

where d(APk, Pn) is the distance between AP k and element n.The offered traffic per AP is calculated as:

∈

=rmSn

nrm TET (2)

In each site m, Nrm cells (transceivers) are then deployed to

fulfill the offered traffic, while not exceeding the maximum number of cells per AP, denoted NAPmax:

= mAPmaxr

rmr

m NC

TN ,,min (3)

where Cr is the capacity per access point of RAT r. Note that if Trm = 0, no access point is deployed. If T

rm > Cr, all the offered

traffic cannot be handled by RAT r. In that case elements are allocated in an increasing order of offered traffic TEn until the maximum capacity per AP CrNAPmax is reached. Remaining traffic that has to be served with other RATs (with a smaller cell radius and higher area capacity) will then belong to ele-ments with the highest traffic TEn. More formally, the traffic elements S

rm are sorted in order of offered traffic and indexed

n�. The number of elements Mrmax,m that are served by AP m is

then determined by:

≤=

=

M

n

APmax,mrnrmax,m NCTEMM

1'

':max (4)

Then, the offered traffic in elements n�=1.. Mrmax m is set to zero

to determine the offered traffic for the next RAT to be de-ployed, i.e.,

rm

rm

nn

Mn

Mn

TETE

max

max

''

'

'0

>

≤= (5)

Once the above deployment is completed, which results in 100% coverage if no traffic remains after the last RAT is de-ployed, the cost for covering smaller fractions of users is calcu-lated. This is done through sorting the access points in order of supported traffic divided by the access point cost. Given the final deployment for full coverage, deployment in this order represents the most cost efficient way to support a given traffic.

It should be noted that no attempts have been made to opti-mize the deployment principle. Instead, the target has been a simple principle that is reasonably good and fair between sys-tem concepts. It could e.g. be noted that limiting W LAN access point positions to a regular grid is probably not optimum, but neither is allowing only one cell radius for cellular macro, mi-cro, and pico cells. This tradeoff is discussed further in [7].

3) Strongly varying, high, and correlated 1) Varying but low (average) traffic density 2) Varying and high

Tra

ffic

Den

sity

Tra

ffic

Den

sit y

Tra

ffic

Den

sit y

Average

Macro cell area capacity

Micro cell

area capacity

Figure 1. Simple example of traffic density variations over space, and deployment of �macro� (light grey) and �micro� (dark grey) access points.

91

III. MODELS AND ASSUMPTIONS

This section describes the user behavior, system, and radio network models used to evaluate the different system concepts. Macroscopic models are used to enable a conceptual compari-son between the concepts for different traffic densities.

A. Traffic Density Models

In order to capture the effects discussed in Section II, a het-erogeneous user behavior is assumed. In short, based on the measurements and model proposed in [4] and statistics from [6], it is assumed that the user density is log-normally distrib-uted around a �large-scale� mean. The �small scale� standard deviation of this distribution is adjusted so that assumed peak values in user density are achieved with reasonable probability. To fit the �cell-level� user density standard deviation to the value 0.4 (log-scale) reported in [4], a spatial correlation is assumed between elements. Reference user densities are cre-ated by multiplying typical suburban (su) and city centre (cc) population densities, 500 and 20.000 inhabitants/km

2 respec-

tively, with an assumed service penetration of 90% and an op-erator market share of 30%. Users are further characterized by an average busy hour traffic intensity, measured in data genera-tion per unit time. As a basis for this, the traffic intensity of a private voice user during busy hour is used. This is assumed to be 20mErlang x 10kbps = 0.2kbps. Multiplying this with a fac-tor N then forms traffic intensity reference values. As a refer-ence, assuming that 0.6% of the monthly traffic is generated during each busy hour (typical for voice), a 1GB/month user corresponds to 13kbps, or N = 66. Traffic density maps (10x10km) are created by multiplying the user densities and per-user traffic intensities. The gray scale contour in Figure 2 depicts a realization of traffic density generated by the model. Note that no explicit service is assumed. The evaluation is

applicable to all services for which the system models, i.e. ac-cess point capacity and coverage, are valid, and that are within the capabilities of the access technology. These capabilities differ significantly between some of the access technologies. For example, 4G concepts should be compared with WCDMA only for services supported by both networks.

B. System and Radio Network Models

Access points of different access technologies are character-ized with different maximum cell radii and capacities; see Ta-ble I (herein a cell is defined as a combination of a sector and carrier frequency). All figures are for the downlink and roughly valid for an urban environment without strict requirements for indoor coverage. However, with the simplified modeling used, without explicit radio network models, the models are applica-ble for arbitrary environment, deployment and service scenar-ios for which the system models are valid. The WCDMA DCH and HS-DSCH figures, assuming a

15MHz spectrum allocation, are taken as reference values, and Long-Term 3G Evolved [8] (henceforth shortly denoted �S3G�) and 4G figures are derived from these. For S3G, a 20MHz spectrum allocation is assumed. Together with a spectrum effi-ciency assumption of 0.75bps/Hz/cell, this results in a capacity per cell of 15Mbps. The same power density as for WCDMA is also assumed, resulting in the same cell radius. To investigate the impact of coverage, a hypothetical S3G system operating in 450MHz spectrum, is also studied. Its cell radius is simply based on frequency difference and a path-loss exponent of 3.5. For 4G, a 100MHz spectrum is assumed, together with a slightly improved spectrum efficiency of 1bps/Hz/cell. This results in a capacity per cell of 100Mbps. A four times lower power density is assumed for the wider 4G carrier than for WCDMA. Assuming a distance attenuation exponent of 3.5, this results in a 30% reduced cell radius. Micro and pico cell capacities are assumed equal to the macro-cell capacities (per cell). The WLAN figures assume single-cell, non-interfered access

points. In coordinated multi-cell scenarios these figures de-crease some 20-40% for 802.11b and 802.11g. In non-coordinated multi-operator scenarios, the capacity is shared equally between the operators. A simple 2-hop regenerative relaying concept is also evalu-

ated. It is assumed that the access point is surrounded by a ring of six relay nodes, each with the same cell radius as a regular macro cell access point. This results in an equivalent cell radius

of √7 of the original cell radius. The capacity is limited by the access point, and assumed to remain at 100Mbps despite the potentially favorable channel conditions towards the relay

Figure 2. A sample of a traffic density map and WCDMA macro and WLAN

access point deployment.

TABLE I. ACCESS POINT CHARACTERISTICS.

Radius Capacity Cost Coeff.

W CDM A DCH macro 1000m [3-9] x 1Mbps 1 (55/45%)

W CDM A DCH micro 250m [1-2] x 1Mbps 0.45 (45/55%)

W CDM A DCH pico 100m 1Mbps 0.3 (35/65%)

W CDM A HS macro 1000m [3-9] x 2.5Mbps 1 (55/45%)

W CDM A HS micro 250m [1-2] x 2.5Mbps 0.45 (45/55%)

W CDM A HS pico 100m 2.5Mbps 0.3 (35/65%)

S3G macro 1000m 3 x 15Mbps 1 (55/45%)

S3G micro 250m 15Mbps 0.45 (45/55%)

S3G pico 100m 15Mbps 0.3 (35/65%)

S3G macro 450 2500m 3 x 15Mbps 1 (55/45%)

4G 700m 3 x 100Mbps 1 (55/45%)

4G micro 175m 100Mbps 0.45 (45/55%)

4G pico 70m 1Gbps 0.3 (35/65%)

4G relay 1850m 100Mbps 6.4 (65/35%)

IEEE 802.11b 40m 6Mbps 0.13 (3/97%)

IEEE 802.11g 40m 22Mbps 0.13 (3/97%)

IEEE 802.11a 20m 22Mbps 0.13 (3/97%)

IEEE 802.11n 20m 100Mbps 0.13 (3/97%)

92 Chapter 6. Heterogeneous Infrastructure Cost (Paper 2)

nodes. Note that more sophisticated relaying concepts than that evaluated here exist, with potential to further improve coverage and capacity.

The access points are also characterized with the cost coeffi-cients given in Table I. These estimate the total infrastructure cost associated with one access point, including CAPEX for radio access network equipment and site build out, as well as OPEX for site rental, transmission, power consumption and O&M over a 10-year period, assuming a 10% discount rate. The figures build on those used in [1], where in turn equipment cost estimates were provided by the Gartner Group and other cost estimates were based on [2]. In this study minor updates for radio network controllers, power consumption and O&M, and addition of W LAN, also based on [2], have been made. The coefficients in Table I are normalized to an estimated value for a cellular macro base station, assumed to be �300k (slightly higher than in [1] due to the above modifications). The components of the cost coefficients are further discussed in [1] and [2], Table I merely includes the fractional CAPEX and OPEX, which in turn are dominated by site and transmission costs respectively. The total infrastructure cost for green-field operators can be calculated as the number of access points of each type multiplied with the corresponding cost coefficients.

Note that this model excludes costs for core network nodes as well as costs for spectrum (due to differences in regulation, a generally applicable spectrum cost model is very difficult to define). This makes the costs incremental, i.e. measuring the additional cost for covering a new area, once the core network and spectrum is paid for. Terminal costs, as well administrative costs, e.g. for marketing and billing, are also excluded.

IV. NUMERICAL RESULTS

In this chapter numerical results are presented on the form infrastructure cost per transferred data unit (1GB) and month versus traffic density and fractional coverage. In Figure 3 90% of the traffic (users) are supported and in Figure 4 only a small fraction, 20%, of the users are served. Some reference levels are marked on the traffic density axis. These are combinations of suburban (su) or city center (cc) environments as defined above, and traffic intensities per user measured in N times voice (0.2kbps). On the cost axis, a reference level of �30 per

GB and month is marked. This is a rough estimate of what a typical user is willing to spend on mobile communications to-day.

Generally, for all system concepts, the infrastructure cost per GB decreases with traffic density while the systems are cover-age limited, and flattens when the system becomes capacity limited.

A. Commercially Available Systems

Beginning with the 802.11g W LAN and 90% of the traffic served, it is seen that for low traffic densities the cost per GB is very high. In a suburban environment with a voice-like traffic intensity per user (su1), the infrastructure cost reaches �10.000/GB/month. To get down to a reasonable cost per GB (�30), a traffic density of 10 Mbps/km

2 is required, approxi-

mately corresponding to su500 or cc10 scenarios. For a fraction of supported users of only 20%, as shown in Figure 4, the cost per user for 802.11g decreases significantly (as expected). The reasonable cost of �30/GB/month is now reached at 1Mbps/km

2 instead, or roughly a cc1 scenario. This indicates

the degree of coverage that can be expected to be profitable for W LAN only operators.

W CDMA DCH and HSDPA yield about 50 times lower cost for moderate traffic densities. These systems reach �30 per GB and month already at 0.2Mbps/km

2 corresponding to su10 sce-

narios. W CDMA HSDPA becomes capacity limited at higher traffic densities than W CDMA DCH, and therefore yields lower costs at high traffic densities. The crossover point be-tween W CDMA HSDPA and 802.11g is about 100Mbps/km

2,

or cc100. W ith 20% of the users covered W CDMA HSDPA is more expensive than W LAN at 30Mbps/km

2 (su1000/cc30),

whereas with 90% coverage W LAN only systems gives a lower cost at first around 100 Mbps/km

2 (cc100).

The multi-access concepts, W CDMA DCH or W CDMA HSDPA combined with 802.11g, are seen to yield the lowest cost of the included subsystems. However, the gain as com-pared to, e.g., a single access W CDMA HSDPA system (with hierarchical cell structures) is evident only at very high traffic (> 300Mbps/km

2). W ith the models and assumptions used,

there is hence no significant multi-access cost reduction. On the other hand there is neither any loss, and there is thus no

10-2

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8 02.11gW C D M A D C H

W C D M A H S

D C H & 11g

H S & 11gS 3G

S 3G 45 0

4G4G r e la y

Figure 3. Infrastructure cost per 1GB/month user versus traffic density for 90% supported users.

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Figure 4. Infrastructure cost per 1GB/month user versus traffic density for 20% supported users.

93

cost drawback for a mobile network operator deploying macro and micro cells first, and then adding W LAN only in hotspots, as compared to a pure W LAN operator. It may also be noted that HSDPA alone is a better solution than both W CDMA DCH and 802.11g for traffic loads up to 100Mbps/km

2.

B. Future Concepts

The S3G concept, with similar coverage and cost character-istics as W CDMA DCH and HSDPA, also yield the same cost as these concepts at low and moderate traffic densities (while the systems are coverage limited). S3G however remains cov-erage limited for higher traffic densities, and yields lower costs for traffic densities exceeding 2Mbps/km

2. S3G is also seen to

be a better alternative than W CDMA HSDPA combined with 802.11g for the full range of studied traffic densities.

The benefit of large coverage is also seen from the hypo-thetical S3G 450 system. Due to its large cell radius, it yields cost almost 10 times lower cost than the other cellular concepts for traffic densities up to around 2Mbps/km

2. Even for high

traffic densities it is better than standard S3G despite the same capacity per AP. This indicates that despite a high mean traffic, there are large areas with less traffic where a large coverage per AP is important.

The preliminary 4G concept, without relaying, is seen to suf-fer somewhat from its reduced coverage for traffic loads up to about 10Mbps/km

2. Beyond this level it yields the lowest cost

per user. The 4G concept with relaying provides slightly lower cost than 4G without relaying for moderate traffic densities, but still higher cost than both W CDMA and S3G.

C. Pricing Strategy and Service Offering Consequences

The cost per data unit can be mapped to a cost per user (and service) in several ways. This is a quite complex area, which has been subject to many studies. Value based pricing is, how-ever, nowadays most often used in practice and there is typi-cally little relation between price and production cost [9]. Yet, the (incremental) production cost will tell if a service would be profitable or not given the end user pricing possibilities.. As-suming a traffic independent cost the average cost per user is given by the total infrastructure cost divided with the number of supported users. This can also be calculated by multiplying the cost per unit data with the average per user traffic intensity. Alternatively, assuming a linearly traffic dependent cost, the cost per individual user is given by multiplying the cost per unit data with the individual user traffic intensity. Several al-ternatives in between these �extremes� of course exist. The re-sults presented here are valid for all these alternatives.

An interesting observation is that, with the models and as-sumptions used, the incremental infrastructure cost for 1GB per month per user can be kept below �30 for traffic densities ex-ceeding about 0.2Mbps/km

2. Adding margins for excluded

costs (marketing, customer care, core network and service plat-forms, profit, taxes, etc.) about 1Mbps/km

2 is probably a more

realistic value. This roughly corresponds to a city center area with today�s voice traffic, or a suburban area with 40 times this traffic per user.

V. CONCLUSIONS

The results of this study indicate that, with the models and assumptions used, single-access solutions with high capacity macro cells yield the lowest costs per user, despite a spatially non-uniform traffic demand. Examples of such systems are W CDMA HSDPA and the preliminary S3G concept. Operator deployed W LAN-only solutions yield high costs even if the requirement on fractions of supported users is small (20%).

Except for at very high traffic, a multi-access network com-posed of W CDMA DCH or HSDPA macro and micro cells combined with IEEE 802.11g access points do not yield lower costs per user than using a single-access W CDMA DCH or HSDPA network consisting of macro, micro and pico cells. This is because several W LAN access points are required in each �hotspot� due to the poor coverage, which results in an excess capacity and poor utilization of each AP. A hotspot con-cept with better coverage could thus lead to better results also for moderate average traffic densities. For mobile network op-erators having a 3G license introducing W LAN hotspots hence need to be motivated by other factors; such as access technol-ogy capabilities and spectrum.

Among the 4G concepts, it is seen that a simple relaying so-lution with macro-like relays nodes may yield improved cost efficiency in areas with moderate traffic demand, up to some 10s of Mbps/km

2. For traffic densities beyond this level, this

particular relaying solution is not motivated. The overall lowest cost is enabled by a hypothetical high-capacity cellular system operating in 450MHz spectrum. This indicates the importance of good coverage, which is of course valid also for alternative means to achieve it.

Future studies could make use of more refined system and economical models. In particular empirical data on traffic de-mand for mobile data services would be useful to improve the heterogeneous traffic density model.

REFERENCES

[1] K. Johansson, et al., �Relation between base station characteristics and cost structure in cellular networks�, in the Proc. of IEEE Personal, Indoor and Mobile Communications (PIMRC), 2004.

[2] F. Loizillon et al., �Final results on seamless mobile IP service provision economics�, IST-2000-25172 TONIC Deliverable no. 11, Oct. 2002.

[3] N. Niebert et al., �Ambient Networks: An Architecture for Communication Networks Beyond 3G�, in IEEE Wireless Communications, Vol. 11, Issue. 2, April 2004, pp. 14-22.

[4] U. Gotzner et al., �Spatial Traffic Distribution in Cellular Networks�, in Proceedings of IEEE Vehicular Technology Conference, 1998.

[5] R. Ganesh and K. Joseph, �Effect of non-uniform traffic distributions on performance of a cellular CDMA system�, Universal Personal Communications Record, October 1997.

[6] US Census Bureau, Table GCT-PH1. �Population, Housing Units, Area, and Density�: 2000, available at http://factfinder.census.gov/,

[7] K. Johansson and A. Furuskär, �Cost efficient capacity expansion strategies using multi-access networks�, in Proceedings of IEEE Vehicular Technology Conferencespring, 2005.

[8] Third Generation Partenership Project (3GPP), RP-040461, �Proposed Study Item on Evolved UTRA and UTRAN�, available at www.3g pp.org/ftp/tsg_ran/TSG_RAN/TSGR_26/Docs/PDF/RP-040461.pdf.

[9] T. T. Nagle and R. K. Holden, "The Strategy and Tactics of Pricing", Second Edition, Prentice Hall, 1997.

Chapter 7

Cost Efficient CapacityExpansion Strategies usingMulti-Access Networks(Paper 3)

Klas Johansson and Anders Furuskar,In Proc. IEEE VTC2005 Spring, May 2005.

95

97

Cost efficient capacity expansion strategies using

m ulti-access netw ork s

K las J oh ansson

W ireless@ K T H , R oyal Institute of T ech nology

E lectrum 4 1 8 , S E - 1 6 4 4 0 K ista, S w ed en

E m ail: k lasj@ rad io.k th .se

A nd ers F urusk ar

W ireless A ccess N etw ork s, E ricsson R esearch

K ista, S w ed en

E m ail: and ers.furusk ar@ ericsson.com

Abstract— Multi-access networks and hierarchical cell struc-tures are two com m on cap acity ex p ansion strateg ies for m ob ilenetwork op erators. I n b oth cases costs can b e m inim iz ed fora set of av ailab le radio access technolog ies, g iv en heterog eneousreq uirem ents on area cov erag e, cap acity and q uality of serv ice. I nthis p ap er we q uantif y the infrastructure cost for a m ulti-accessnetwork com p osed of m acro cellular H S D P A b ase stations andI E E E 8 0 2 .1 1 g W L A N access p oints. T he network is dim ensionedfor an urb an env ironm ent using a stochastic m odel for hetero-g eneous traffi c density .

W ith the used assum p tions and m odelling it is shown thata com b ination of H S D P A b ase stations dep loy ed with 4 0 0 mcell radius tog ether with W L A N in hot sp ots are suffi cient forav erag e traffi c densities up to around 5 0 Mb p s/km 2 ( 5 0 tim esthe traffi c of ty p ical p riv ate v oice users today ). I n order toev aluate the sensitiv ity to different desig n features, we introducethe elasticity of infrastructure cost and can thereb y show that itis m ore im p ortant to im p rov e cap acity in H S D P A than cov erag ep er 8 0 2 .1 1 g access p oint. H owev er, with a sp arse dep loy m entof H S D P A m acro cells ( 8 0 0 m radius) infrastructure cost ism ore elastic to 8 0 2 .1 1 g cov erag e. T he p ap er also indicatessom e p ossib ilities to differentiate future radio access technolog iestowards current sy stem s.

I . I N T R O D U CT I O N

M ulti-access netw ork s are prom ising d ue to h igh ly v arying

req uirem ents ov er tim e and geograph ically on m ob ility, q uality

of serv ice, capacity, etc., and th e inh erent trad eoff in all

w ireless system s b etw een range and feasib le d ata rates. H ence,

b y d eploying a h eterogeneous w ireless netw ork , w ith m ultiple

stand ard s and /or h ierarch ical cell structures, an operator can

ad apt capacity to d em and and th ereb y low er th eir capital and

operational expend itures (CA P E X /O P E X ) . T h ere are in fact

m any w ireless stand ard s in th e m ark et alread y tod ay, and

ev en m ore are und er d ev elopm ent. S om e system s, lik e 3 G ,

are d esigned to b enefit from econom y of scope, m eaning th at

cost efficiency is ach iev ed since a w id e range of serv ices can

b e prov id ed w ith th e sam e system ov er w id e areas, poten-

tially serv ing m any users. O th er stand ard s are stream lined for

specific serv ices, such as 2 G for w id e-area m ob ile v oice and

W L A N for local h igh -speed d ata connectiv ity. T h ere are h ence

reasons to b eliev e th at m ulti-access netw ork s is a sustainab le

d eploym ent strategy for m ob ile netw ork operators.

T h is paper treats cost efficient d eploym ent strategies for

netw ork s com posed of H S D P A m acro cellular b ase stations

(B S ) and I E E E 8 0 2 .1 1 g access points (A P ) . T h ese sh ould

represent system s w ith long range for w id e area cov erage and

low cost, sh ort range access points suitab le for h ot spots. M ore

specifically, tw o prob lem s w ill b e ad d ressed ; ( i) th e trad eoff

b etw een th e num b er of m acro cellular B S s and com plem entary

W L A N A P s need ed , and (ii) w h ich param eters th at are m ost

im portant to im prov e in each system in ord er to furth er

red uce costs. T h is stud y com plem ents th e results presented

in [3 ] , w h ich ev aluates th e cost w ith single and m ulti-access

d eploym ent for a num b er of, b oth com m ercially av ailab le, and

future rad io access tech nologies. In b oth stud ies th e scope is

lim ited to th e rad io access netw ork . S pectrum license fees and

oth er costs th at are com m on for th e w h ole m ob ile netw ork

are th us exclud ed . L ik ew ise are a num b er of oth er param eters

th at also are of im portance for a m ob ile operator’s d eploym ent

strategy; e.g. topology, prev ious assets, and (perh aps forem ost)

d em and and regulatory req uirem ents. Y et, th e ob jectiv e is to

contrib ute to a b etter und erstand ing of th e role of m ulti-access

as capacity expansion strategy and for th is reason a stoch astic

(log-norm al) spatial d istrib ution of traffic is assum ed [ 3 ] .

S erv ice allocation principles for m ulti-access netw ork s h av e

prev iously b een treated in, e.g., [ 4 ] and [ 7 ] . T h ese stud ies,

h ow ev er, ad d resses th e prob lem of selecting rad io access

netw ork for users th at are cov ered b y m ultiple system s, and not

d im ensioning of each sub system . In ad d ition, E U h as recently

initiated th e A m b ient N etw ork s project w h ich d eals w ith a

num b er of aspects, m ainly tech nically b ut also b usiness w ise,

of h eterogeneous netw ork s [ 9 ] and th ere are h ence a num b er

of ongoing stud ies in th is area. P rev ious stud ies consid ering

th e cost structure of m ob ile system s includ e, e.g., [ 6 ] , [ 8 ] , [ 1 1 ] ,

and [ 1 2 ] . F rom th ese stud ies it is clear th at th e cost structure

of m ob ile netw ork s tod ay is d om inated b y th e rad io access

netw ork . M oreov er, th e stud ies in [6 ] and [ 8 ] prov id e a b asis

for th e tech no-econom ical m od elling used in th e seq uel of th is

paper w h ich is outlined as follow s.

S ection II cov ers b asic m od elling and assum ptions related to

B S perform ance and costs, as w ell as netw ork d im ensioning.

Infrastructure cost estim ations for a m ulti-access netw ork is

presented in S ection II I for incum b ent and greenfield operators

respectiv ely, togeth er w ith an analysis of th e elasticity of cost

w ith respect to th e capacity, cov erage and cost per B S . In

S ection IV w e d iscuss h ow th e stud ied system s could b e

im prov ed tech nically and econom ically, and point at a few

gaps th at potentially could b e filled b y future rad io access

tech nologies. T h e paper is conclud ed in S ection V .

98 Chapter 7. Cost Efficient Capacity Expansion (Paper 3)

II. SY ST E M MO D E L S AN D PE R F O R MAN C E ME ASU R E S

A m a c r o s c o p ic m o d e l is u s e d to c a p tu r e k e y te c h n ic a l a n d

e c o n o m ic a l p a r a m e te r s th a t in fl u e n c e th e in f r a s tr u c tu r e c o s t f o r

a ty p ic a l ( w e s te r n E u r o p e a n ) m o b ile n e tw o r k o p e r a to r. T h is is

b a s e d o n p r e v io u s w o r k p r e s e n te d in [ 4 ] a n d fa c to r s n o r m a lly

m o d e lle d in n e tw o r k d im e n s io n in g a n d c a p a c ity a n a ly s is ; lik e

in te r f e r e n c e , p r o p a g a tio n , e tc ., a r e e x o g e n o u s to th e m o d e l.

A. Network dimensioning and traffic modelling

In a m u lti- a c c e s s n e tw o r k APs w ith s h o r te r r a n g e a n d c o s t

m a y b e u s e d in h o t s p o ts , w h e r e a s m a c r o B Ss w ith a h ig h

a r e a c o v e r a g e a r e u s e d to p r o v id e b a s ic c o v e r a g e a n d c a p a c ity .

In p r a c tic e r a d io n e tw o r k p la n n in g a n d d im e n s io n in g is a n

ite r a tiv e p r o c e s s [ 2 ] a n d a s th e n e tw o r k m a tu r e s it is g r a d u a lly

a d a p te d to lo c a l d e m a n d . T o m o d e l th e d e p lo y m e n t s tr a te g y w e

u s e a h e u r is tic a lly b a s e d m e th o d w h ic h is d e s c r ib e d in m o r e

d e ta il in [ 3 ] . T h e b a s ic id e a is , in o u r e x a m p le , to fi r s t d e p lo y

H SD PA s ite s w ith a g iv e n c e ll r a d iu s a n d lo a d th e m w ith a s

m a n y tr a n s c e iv e r u n its a s n e e d e d ( u p to th e m a x im u m c a p a c ity

p e r B S). If th is is n o t s u ffi c ie n t to s e r v e th e tr a ffi c d e m a n d ,

8 0 2 .1 1 g APs a r e d e p lo y e d in a r e a s w ith h ig h e s t tr a ffi c d e n s ity .

Id e a lly , e a c h s u b s y s te m s h o u ld b e ju s t c a p a c ity lim ite d f o r a

c o s t e ff e c tiv e d e p lo y m e n t to a v o id e x c e s s c a p a c ity p e r AP.

T r a ffi c d e n s ity is m o d e lle d a s in [ 3 ] w ith a lo g - n o r m a l,

s p a tia lly c o r r e la te d , s to c h a s tic v a r ia b le o v e r th e s e r v ic e a r e a

( 1 0 x 1 0 k m ) w h ic h h e r e in is d iv id e d in to s a m p le s o f 2 0 x 2 0 m .

A s ta n d a r d d e v ia tio n o f 7 d B h a s b e e n a s s u m e d a n d th e c o r r e -

la tio n d is ta n c e is 5 0 0 m , w h ic h m a tc h e s th e c e ll le v e l s ta tis tic s

p r e s e n te d in [ 5 ] . In a ll n u m e r ic a l e x a m p le s th e a v e r a g e p o p -

u la tio n d e n s ity is 2 0 0 0 0 in h a b ita n ts /k m 2, c o r r e s p o n d in g to a

c ity c e n te r e n v ir o n m e n t. T h e o p e r a to r u n d e r s tu d y is a s s u m e d

to h a v e a 3 0 % m a r k e t s h a r e a n d th e s e r v ic e p e n e tr a tio n is

9 0 % . H e n c e , th e n u m b e r o f s u b s c r ib e r s is in a v e r a g e 5 4 0 0

u s e r s /k m 2 ( lo c a lly th is is m u c h h ig h e r ) .

B . P ath loss models

Alth o u g h n o t u s e d e x p lic itly f o r th e n e tw o r k d im e n s io n in g ,

tw o s ta n d a r d p a th lo s s m o d e ls w ill b e u s e d to e s tim a te th e

c e ll r a n g e o f m a c r o c e lls in Se c tio n IV ; th e C O ST 2 3 1 - H a ta

( v a lid f o r 1 k m < d < 2 0 k m ) a n d C O ST 2 3 1 - W a lfi s c h -Ik e g a m i

( v a lid f o r 2 0 m < d < 5 k m ) . In g e n e r a l th e p a th lo s s in s m a ll

c e lls , a n d in p a r tic u la r in d o o r s , is c a s e s p e c ifi c a n d n o t r e a d ily

d e s c r ib e d w ith a s ta tis tic a l m o d e l. H o w e v e r, th e s e c o m m o n ly

u s e d m o d e ls p r o v id e d in [ 1 ] s h o u ld g iv e a n in d ic a tio n o f

f e a s ib le c e ll r a n g e s a n d th e s a m e m e th o d o lo g y h a s b e e n u s e d

in , e .g ., [ 2 ] f o r r u d im e n ta r y c o v e r a g e a n a ly s is .

C O S T 2 3 1 - W alfisch - Ikegami - a s s u m in g b u ild in g s e p a r a tio n

( 3 0 m ) , s tr e e t w id th ( 1 5 m ) , b u ild in g h e ig h t ( 2 5 m ) , a n d a 9 0

d e g r e e a n g le o f a r r iv a l f o r b u ild in g r e fl e c tio n s :

Lb = 57.9 + (2 7.5 −

1.5fc

9 2 5) lo g

1 0fc + 3 8 lo g

1 0d. ( 1 )

C O S T 2 3 1 - H ata - w ith 3 d B m e tr o p o lita n a r e a c o r r e c tio n

fa c to r a n d a n O k a m u r a - H a ta B fa c to r a s r e c o m m e n d e d f o r

la rg e c itie s :

Lb = 2 8 .9 + 3 3 .9 lo g1 0

fc + 3 5.2 lo g1 0

d. ( 2 )

T AB L E I

AC C E SS PO IN T C H AR AC T E R IST IC S

HSDPA 8 0 2 .1 1 g

R a d iu s 2 0 0 - 1 0 0 0 m 4 0 m

C a p a c it y [ 3 - 9 ] x 2 .5 Mb p s 2 2 Mb p s

C o s t c o e f fi c ie n t 1 ( 5 5 % /4 5 % ) 0 .1 3 ( 3 % /9 7 % )( C APE X /O PE X ) + 0 .0 3 p e r c e ll

In b o th m o d e ls th e B S h e ig h t w a s 3 0 m a n d m o b ile s ta tio n

h e ig h t 1 .5 m . Lb d e n o te p a th lo s s in d B , fc is th e c a r r ie r

f r e q u e n c y in MH z , a n d d is th e d is ta n c e b e tw e e n B S a n d

m o b ile s ta tio n g iv e n in k m .

C . Access p oint p erformance and cost assu mp tions

APs a r e c h a r a c te r iz e d w ith d iff e r e n t c e ll r a d ii, c a p a c itie s a n d

c o s ts ; s e e T a b le I. C a p a c ity c o e ffi c ie n ts f o r 8 0 2 .1 1 g a s s u m e s

n o c o - c h a n n e l in te r f e r e n c e w h e r e a s H SD PA d o e s , d u e to th e

c e llu la r d e p lo y m e n t a n d lim ite d f r e q u e n c y s p e c tr u m – w e

a s s u m e 3 c a r r ie r s x 5 Mh z ( 1 5 MH z in to ta l) f o r d o w n lin k .

N o tic e th a t th e m a x im u m c a p a c ity f o r H SD PA a n d 8 0 2 .1 1 g

is s im ila r, 2 2 .5 a n d 2 2 Mb p s , s o th e AP w ith lo w e s t c o s t p e r

tr a n s m itte d b it is e s s e n tia lly d e te r m in e d b y th e g e o g r a p h ic a l

d is tr ib u tio n o f tr a ffi c .

C o s t c o e ffi c ie n ts in c lu d e b o th C APE X a n d O PE X a n d a r e

h e n c e f o r th d e n o te d AP c o s t. F o r H SD PA w e u s e th e c o s t f o r a

m a c r o B S d e r iv e d in [ 6 ] , w h ic h in tu r n w a s b a s e d o n e s tim a te s

p r o v id e d b y th e G a r tn e r G r o u p a n d [ 8 ] . In th e n u m e r ic a l

e x a m p le s w e h a v e a s s u m e d th a t a m a c r o B S c o s ts e3 0 0 k .

C o s ts f o r r a d io n e tw o r k c o n tr o lle r s ( R N C ) a n d e le c tr ic a l p o w e r

h a v e b e e n a d d e d a s c o m p a r e d to th e e s tim a te s in [ 6 ] . T a b le

I a ls o s u m m a r iz e s th e c o s t s tr u c tu r e in te r m s o f C APE X

a n d O PE X a n d th e a d d itio n a l c o s t f o r e x tr a c e lls ( d e fi n e d

a s a c a r r ie r f r e q u e n c y a n d s e c to r ) in H SD PA. An in c u m b e n t

o p e r a to r th a t a lr e a d y h a s s ite s f o r le g a c y s y s te m s in s ta lle d m a y

r e u s e m o s t o f th e s e s ite s a n d w e a s s u m e th a t th is lo w e r s th e

c o s t f o r H SD PA B Ss w ith 2 5 % . F o r 8 0 2 .1 1 g n e w e s tim a te s

h a v e b e e n d e d u c te d b a s e d o n [ 8 ] . O PE X is c a lc u la te d in

p r e s e n t v a lu e o v e r a 1 0 - y e a r p e r io d , u s in g a 1 0 % d is c o u n t

r a te ( s e e f u r th e r [ 6 ] ) . F o r th e s a k e o f s im p lic ity th e n e tw o r k is

d im e n s io n e d to c a r r y th e s a m e tr a ffi c d u r in g th e w h o le n e tw o r k

lif e s p a n .

D . Infrastru ctu re cost measu res

T h e b a s ic m e a s u r e f o r c o s t e ffi c ie n c y u s e d is th e infrastru c-

tu re cost p er G B and month . In d o in g th is w e a s s u m e th a t 0 .6 %

o f th e m o n th ly tr a ffi c is c a r r ie d d u r in g e a c h b u s y h o u r, w h ic h

r o u g h ly c o r r e s p o n d s to th e tr a ffi c p a tte r n in c u r r e n t c e llu la r

s y s te m s , a n d th a t th e n e tw o r k is d im e n s io n e d a c c o r d in g to

a v e r a g e a g g r e g a te th r o u g h p u t ( p e r a r e a s a m p le ) . H e n c e , th e

r e s u lts a n d c o n c lu s io n s s h o u ld h o ld f o r a ll tr a ffi c m ix e s th a t

fa ll w ith in th e p e r f o r m a n c e p a r a m e te r s g iv e n in T a b le I.

As a s e n s itiv ity a n a ly s is w e e s tim a te th e elasticity of in-

frastru ctu re cost. E la s tic ity is c o m m o n ly u s e d in e c o n o m ic s

to m e a s u r e th e in c r e m e n ta l p e r c e n ta g e c h a n g e in o n e v a r ia b le

w ith r e s p e c t to a n in c r e m e n ta l p e r c e n ta g e c h a n g e in a n o th e r

v a r ia b le [ 1 0 ] . W e d e fi n e th e e la s tic ity o f a p a r a m e te r X ( w h ic h

99

100

101

102

100

101

102

A v e ra g e T ra ffic D e n s ity [M b p s /k m2]

Infr

astr

uctu

re C

ost

pe

r M

on

th a

nd

GB

[E

uro

]

V o ic e 10 x v o ic e 100 x v o ic e

200m

4 00m

8 00m

1000m

Fig. 1. Infrastructure cost per GB and month for an incumbent operator witha multi-access network consisting of H SD P A macro BSs and IE E E 8 0 2 .11gA P s. T he curv es depict different cell radii in the macro cells.

herein is either cost, cov erage or capacity per A P ) on the total

infrastructure cost C as:

EC,X =∆C/ C

|∆X| / X. ( 3 )

T hus, a negativ e EC,X corresponds to a decreased cost and

if EC,X is positiv e the infrastructure cost increases (indepen-

dently of if the changed v ariable X is increased or decreased).

T hus, the higher absolute elasticity , the greater impact X has

on C. N otice that elasticity q uite often is calculated in absolute

v alue. A 5 0 % change in X has been used in all studied cases so

that |∆X|/ X = 0 .5. A s an ex ample, assume that we want to

estimate the elasticity with respect to A P cov erage in 8 0 2 .11g.

EC,X = −1 would then mean that the total infrastructure cost

C decreases with 5 0 % if the cell area is doubled. I.e., if the

A P range were 4 0 ·√

1.5 = 4 9 m instead of 4 0 m.

III. N U M E RIC A L RE SU L T S

In this section the tradeoff between H SD P A cell radius (site

density ) and the number of 8 0 2 .11g A P s will fi rst be q uantifi ed

through simulations using the models outlined abov e, which

are described more thoroughly in [3 ] . T hen the elasticity of

infrastructure cost is deriv ed for a few base line confi gurations.

A. Traffic density and HSDPA site distance

T he macro cellular site density that minimiz es cost with

single access deploy ment is not necessarily optimum with a

multi-access deploy ment. It depends on the traffi c demand,

and how that v aries ov er the total serv ice area (as discussed in

[3 ] ) . If the macro BSs are deploy ed too sparsely , the remaining

areas that hav e to be cov ered with, in this case, 8 0 2 .11g A P s

may be too large with a resulting ov ercapacity per access point.

T his tradeoff is illustrated in Figure 1, where the infrastructure

cost per GB and month is shown for an incumbent operator

with different cell radius in the macro cell lay er and 9 0 % of

T A BL E II

SU M M A RY O F M O N T H L Y IN FRA ST RU C T U RE C O ST S P E R GB A N D T H E C O ST

A D V A N T A GE FO R IN C U M BE N T S T O W A RD S GRE E N FIE L D O P E RA T O RS.

Voice 1 0 x v oice 5 0 x v oice

T r a f fi c d en s it y 1M bps/k m2 10 M bps/k m2 5 0 M bps/k m2

H S D P A r a d iu s 10 0 0 m 8 0 0 m 4 0 0 m

H S D P A B S d en s it y 0 .3 3 BSs/k m2 0 .5 6 BSs/k m2 2 .2 BSs/k m2

H S D P A cells /B S 1.4 cells 5 .7 cells 6 .8 cells

W L A N A P d en s it y 0 A P s/k m2 2 .1A P s/k m2 19 A P s/k m2

I n cu m b en t op er a tor e6 .8 e2 .9 e1.7

G r een fi eld op er a tor e8 .8 e3 .4 e1.9

C os t a d v a n t a g e 2 4 % 15 % 10 %for in cu m b en t

the offered traffi c supported. A s ex pected the cost v aries with

traffi c density and deploy ed macro cell radius. T he results are

summariz ed in T able II for 1, 10 and 5 0 times the traffi c of

ty pical priv ate v oice telephony users (2 0 mE rl, 10 k bps, see [3 ] ) .

A t 10 x v oice, in this ex ample eq ual to a traffi c density

of 10 M bps/k m2, an H SD P A cell radius of 8 0 0 m y ields the

lowest cost. In av erage six cells (two carriers in three sectors)

are used per H SD P A BS and there are approx imately two

8 0 2 .11g A P s per k m2 deploy ed in hot spots. T he lowest cost

for another tenfold traffi c increment is achiev ed with 4 0 0 m

cell radius. T he av erage number of cells per H SD P A BS

is approx imately the same. H owev er, the number of W L A N

A P s is now 19 /k m2. N otice also that the incremental cost

per transmitted GB fl attens when the macro cellular network

becomes capacity limited which is due to poor cov erage in

8 0 2 .11g.

Results for Greenfi eld operators hav e the same shape and

the difference in infrastructure cost per GB and month is giv en

T able II. T he cost adv antage of incumbents v anishes as traffi c

increase and 8 0 2 .11g has to be deploy ed to a greater ex tent.

T his highlights how important it is for incumbents to acq uire

more spectrum to remain competitiv e if traffi c surges in the

long run.

T hese results also ex plain how operators could ex ploit

W L A N in the short run instead of building a denser macro

network if traffi c suddenly increases. In particular, considering

the relativ ely small sunk costs in W L A N , see T able I, this

could be economically justifi ed during transition periods.

For ex ample, increasing capacity from 5 to 10 M bps/k m2

with W L A N instead of deploy ing more macro sites increase

costs with less than 10 0 % per GB calculated ov er 10 y ears

(comparing the results for 10 0 0 m and 8 0 0 m H SD P A radii) .

In the long run increasing capacity in the macro cell lay er is,

howev er, more cost effi cient as we will discuss further nex t.

B . E lasticity o f infrastru ctu re co st

T wo reference sy stems adapted for approx imately 10 and

5 0 M bps per k m2 will be used as ex amples to analy z e what k ey

parameters that would lower infrastructure costs the most. For

these sy stems, with 8 0 0 m and 4 0 0 m cell radius respectiv ely ,

the elasticity of infrastructure cost EC,X is plotted Figure 2

with the following v ariables changed (one per curv e):

• decreased H SD P A cost coeffi ecient,

100 Chapter 7. Cost Efficient Capacity Expansion (Paper 3)

100

101

102

1

0.5

0

0.5

1


Ela

sticity o

f In

fra

str

uctu

re C

ost


H S D P A c o s t

H S D P A c a p a c ity

8 02.11g c o s t

8 02.11g c o v e ra g e

(a) HSDPA cell radius 400m

100

101

102

1

0.5

0

0.5

1


Ela

sticity o

f In

fra

str

uctu

re C

ost


H S D P A c o s t

H S D P A c a p a c ity

8 02.11g c o s t

8 02.11g c o v e ra g e

(b ) HSDPA cell radius 8 00m

F ig . 2 . E lasticity o f in frastructure co st f o r an HSDPA an d 8 02 .1 1 g multi-access n etw o rk w ith resp ect to differen t ch an g es in desig n p arameters. T h e referen cesy stem is adap ted fo r ap p ro x imately 5 0 x v o ice traffi c (left g rap h ) an d 1 0 x v o ice traffi c (rig h t g rap h ). All b ut th e ch an g ed v ariab les are k ep t co n stan t.

• decreased 8 02 .1 1 g co st co effi ecien t,

• in creased HSDPA B S cap acity , an d

• in creased 8 02 .1 1 g AP co v erag e.

E lasticity o f in frastructure co st w ith resp ect to th e co st co -

effi cien ts sh o w h o w larg e sh are o f th e in frastructure co st

th at stems fro m each sy stem. W ith 400m cell radius each

sub sy stem stan ds fo r 5 0% o f th e co st at 9 0M b p s/k m2, w h ile

th e cro sso v er o ccurs at aro un d 1 5 M b p s/k m2 w ith 8 00m cell

radius. At 1 M b p s/k m2 o n ly HSDPA b ase statio n s are dep lo y ed

an d th ere is h en ce o v er-cap acity in th e macro cell lay er. W e can

also see th at HSDPA cap acity is mo re imp o rtan t to imp ro v e

th an 8 02 .1 1 g co v erag e at all studied traffi c den sities w ith 400m

HSDPA cell radius. W ith mo re sp arsely dep lo y ed HSDPA

sites, h o w ev er, in frastructure co st is mo re elastic to 8 02 .1 1 g

AP co v erag e th an HSDPA B S cap acity f o r traffi c den sities

ab o v e 5 0M b p s/k m2.

Alth o ug h n o t in cluded in th e g rap h s, simulatio n s also

sh o w th at th e co st is p erfectly in elastic to th e cap acity f o r

8 02 .1 1 g up to ap p ro x imately 2 00M b p s/k m2. N o te, th o ug h ,

th at b uildin g o p erato r dep lo y ed W L AN n etw o rk s w ith full

co v erag e p ro b ab ly is n o t feasib le co n siderin g th e g reat n umb er

o f APs req uired so such a n etw o rk is a b it h y p o th etical. Hig h er

traffi c den sities th an , e.g ., 5 0M b p s/k m2 are n o t lik ely to b e

imp lemen ted usin g th e studied set o f radio access tech n o lo g ies

o n ly . I n stead th e results f o r h ig h er traffi c den sities sug g est

h o w cellular an d W L AN lik e so lutio n s sh o uld b e imp ro v ed

if traffi c in crease in th e future. I n p articular, w e can see h o w

imp o rtan t ex istin g assets (i.e. p rev io us dep lo y men t) o f mo b ile

n etw o rk o p erato rs an d req uiremen ts o n traffi c den sity are fo r

th e selectio n o f radio access tech n o lo g y .

I V . PO T E N T I AL I M PR O V E M E N T S O F C U R R E N T SY ST E M S

F o llo w in g th is an aly sis o f k ey p arameters to imp ro v e in

HSDPA an d 8 02 .1 1 g if traffi c in creases w e w ill discuss b riefl y

h o w such imp ro v emen ts co uld b e materializ ed.

A. Capacity and coverage

T o start w ith , cap acity (ag g reg ate th ro ug h p ut) p er site is o f

co urse a majo r issue fo r macro cells if traffi c deman d in creases

sig n ifi can tly . I n th e lo n g run th is is p erh ap s easiest so lv ed v ia

mo re sp ectrum b an dw idth w h ich , h o w ev er, usually co mes w ith

a co n siderab le co st. Adv an ced tran smitter an d receiv ers tech -

n iq ues, lik e M ultip le-I n p ut- M ultip le-O utp ut (M I M O ) sy stems,

are also p ro misin g . N aturally it is also b en efi cial to smo o th en

o ut traffi c lo ad o v er time, e.g . usin g in ter- temp o ral p ricin g

sch emes o r cach in g an d p re-fetch in g so lutio n s.

M erely in creasin g cap acity p er site is n o t suffi cien t th o ug h .

Also th e cell ran g e n eeds to b e main tain ed to p ro v ide co v erag e

also f o r h ig h er p eak data rates. T h is req uires an imp ro v ed

lin k b udg et w h ich , e.g ., can b e ach iev ed th ro ug h M I M O ,

h ig h er masts, freq uen cy sp ectrum in lo w er b an ds, o r multi-

h o p relay in g . Ab o v e all th e lin k b udg et is v ery critical if

b ro adb an d data serv ices sh o uld b e p ro v ided in do o rs usin g

o utdo o r B Ss. T h is is illustrated in F ig ure 3 , w h ere w e h av e

p lo tted th e th eo retical cell ran g e in up lin k f o r a co n v en tio n al

W C DM A macro cell. A few ty p ical serv ices are dep icted in

th e g rap h acco rdin g to stan dard lin k b udg ets p resen ted in

[ 2 ] . Already at 1 44k b p s th e max imum cell ran g e is in th e

o rder 7 00m fo r in do o r users. Hen ce, in creasin g p eak data

rate to 1 M b p s reduces th e n o min al cell ran g e fro m almo st

7 00m to ap p ro x imately 3 5 0m sin ce th e lin k b udg et is lin early

p ro p o rtio n al to th e data rate (all else eq ual). T o sup p o rt ev en

h ig h er rates in a macro cellular n etw o rk th e lin k b udg et

th erefo re n eeds to b e imp ro v ed.

B . Cos t per acces s point

C o n tin uin g to th e co st p er AP, w e h av e in Sectio n I I I - B

co n sidered a reductio n o f th e to tal co st, in cludin g in v estmen ts

an d run n in g co sts. I n T ab le I th e main co st co effi cien ts f o r th e

studied sy stems w ere listed. T h e b ase v alue h as b een e3 00k

101

120 125 13 0 13 5 14 0 14 5 15 0200

4 00

6 00

8 00

1000

1200

14 00

16 00

18 00

2000

2200

A llo w e d p a th lo s s [d B ]

Ce

ll ra

ng

e [

m ]

C O S T 23 1-W a lfis c h -Ik e g a m i

C O S T 23 1-H a ta

In d o o r 14 4 k b p s re a ltim e d a ta u s e r

O u td o o r 3 8 4 k b p sn o n re a ltim e d a ta u s e r

In c a r 12.2k b p s s p e e c h u s e r

Fig. 3. Uplink range as a function of allowed path loss for urban WCDMAm acro cells. A few ty pical serv ices [2 ] are depicted in thegraph.

for a single carrier H S DP A B S with an additional e1 0 k per

cell and e39 k for 8 0 2 .1 1 g AP s.

As discussed in [6 ] , the cost structure for new m acro

cells is today dom inated by costs for site acq uisition, build-

out, installation and rental. In 8 0 2 .1 1 g howev er, ’last m ile’-

transm ission is a key contributor to the total infrastructure

cost (followed by site rental) . T his is based on an assum ption

that a leased line is req uired per AP . L ow-cost transm ission

alternativ es, e.g. wireless (m eshed) networks, could reduce

O P E X to som e ex tent. B ut it does not change the conclusion

that cov erage per 8 0 2 .1 1 g AP needs to be im prov ed (with

retained capacity ) in order to lower the cost for operator

deploy ed WL AN solutions. An alternativ e m ethod to prov ide

indoor cov erage and capacity at a low cost could be to let users

install AP s in their own prem ises that are open for access to

all the operator’s subscribers and roam ing partners, e.g. using

a N etwork Franchise business m odel. T his solution seem s

prom ising in particular for operators with a strong position

also in fi x ed access.

C. Positioning of next generation radio access technologies

T he results also points at how a future 4 G radio interface

targeted for urban env ironm ents could be differentiated with

respect to current m ain stream technologies for wireless data

connectiv ity . E x am ples of niches not cov ered well by today ’s

sy stem s for urban deploy m ent are

• high capacity m icro cells, and

• long range WL AN access points.

In both cases we assum e that the data rate at the cell border

is signifi cantly higher than in 3G . Without m aking ex plicit

assum ptions on req uired range and data rates, we can note

that concepts sim ilar to the gaps indicated by this study already

hav e been proposed in different contex ts; for ex am ple in the

research program WIN N E R (recently initiated by the E U) and

in sim ilar initiativ es. N um erical results on the infrastructure

cost for a different 4 G concepts are presented in [3].

V . CO N CL US I O N S

Multi-access networks are useful in order to lower infras-

tructure costs for operators in the long run if geographical

traffi c density v aries strongly . Also in the short run, it can be

benefi cial as a tem porary solution before im prov ed m acro cell

networks and m ore freq uency spectrum are av ailable.

As an ex am ple, we hav e looked in m ore detail into an

operator deploy ed network with m acro cellular H S DP A base

stations and IE E E 8 0 2 .1 1 g access points. For this sy stem

the total infrastructure cost was q uantifi ed for a city center

env ironm ent using a stochastic (log-norm al) m odel for het-

erogeneous traffi c density . I t was shown that an H S DP A cell

radius between 4 0 0 m and 8 0 0 m m inim iz e cost for av erage

traffi c densities during busy hour of 1 0 - 5 0 Mbps/km 2. T his

approx im ately correspond to 1 0 - 5 0 tim es the traffi c of priv ate

v oice users today . For higher traffi c densities, either a v ery

dense m acro cell lay er, or a large am ount of WL AN access

points are needed and this is probably not feasible.

We hav e also illustrated how elasticity of infrastructure cost

can be used to effectiv ely analy z e what design param eters

that are m ost im portant to im prov e in a m ulti-access wireless

network. In the ex am ple with H S DP A and 8 0 2 .1 1 the capacity

per m acro base station is m ore im portant to im prov e with

4 0 0 m cell radius than 8 0 2 .1 1 g cov erage up to 1 0 0 Mbps/km 2.

H owev er, if H S DP A base stations are m ore sparsely deploy ed

(8 0 0 m radius) the sam e cost sav ings can be achiev ed through

increasing the range of 8 0 2 .1 1 g already at 5 0 Mbps/km 2.

ACK N O WL E DG ME N T

T his work has partly been sponsored by the S wedish

Foundation for S trategic Research v ia the Affordable Wireless

S erv ices and Infrastructure P roject.

RE FE RE N CE S

[ 1 ] CO S T 2 31 Final Report, av ailable at http://www.lx .it.pt/cost2 31 /[ 2 ] H . H olm a and A. T oskala (editors), ” W CD M A for U M T S ” , T hird edition,

J ohn Wiley & S ons, 2 0 0 4 .[3] A. Furuskar, M. Alm gren, and K . J ohansson, ” An Infrastructure Cost

E v aluation of S ingle- and Multi-Access N etworks with H eterogeneousUser B ehav ior” , in Proc. IE E E V ehicu lar T echnology Conference sp ring,May 2 0 0 5 .

[ 4 ] A. Furuskar, ” Allocation of m ultiple serv ices in m ulti-access wirelesssy stem s” , in Proc. IE E E W ork shop on M ob ile and W ireless Com m u ni-

cations N etw ork , 2 0 0 2 .[ 5 ] U. G otz ner et al., ” S patial T raffi c Distribution in Cellular N etworks” , in

Proc. IE E E V ehicu lar T echnology Conference, 1 9 9 8 .[ 6 ] K . J ohansson, A. Furuskar, P . K arlsson, and J . Z ander, ” Relation between

base station characteristics and cost structure in cellular networks” ,in Proc. IE E E Personal, Indoor and M ob ile R adio Com m u nications,S ept. 2 0 0 4 .

[ 7 ] J . K alliokulju et al., ” Radio Access S election for Multistandard T erm i-nals” , I E E E Com m u nications M agaz ine, O ct. 2 0 0 1 .

[ 8 ] F. L oiz illon et al., ” F inal resu lts on seam less m ob ile IP serv ice p rov ision

econom ics” , I S T - 2 0 0 0 - 2 5 1 7 2 T O N IC Deliv erable num ber 1 1 , O ct. 2 0 0 2 .[ 9 ] N . N iebert et al., ” Am bient N etworks: An Architecture For Com m u-

nication N etworks B ey ond 3G ” , I E E E W ireless Com m u nications, April2 0 0 4 .

[ 1 0 ] R. S . P indy ck and D. L . Rubinfeld, ” M icroeconom ics” , Fifth edition,P rentice H all International, 2 0 0 1 .

[ 1 1 ] D. P . Reed, ” T he Cost S tructure of P ersonal Com m unication S erv ices” ,I E E E Com m u nications M agaz ine, Apr. 1 9 9 3.

[ 1 2 ] J . Z ander, ” O n the cost structure of future wideband wireless access” ,in Proc. IE E E V ehicu lar T echnology Conference, 1 9 9 7 .

Chapter 8

On the Cost Efficiency ofUser Deployed AccessPoints Integrated in MobileNetworks (Paper 4)

Klas Johansson,In Proc. RVK 05, June 2005.

103

105

On th e c o s t e ffi c ie nc y o f u s e r d e p lo ye d a c c e s s

p o ints inte g r a te d in m o b ile ne tw o r k s

Klas J o h an sso n

Wireles s @ K TH , R oyal In s titu te of Tec h n ology, E lec tr u m 4 1 8 , S E - 1 6 4 4 0 K is ta, S wed en

E m ail: k las j@ rad io.k th .s e

Abstract— O p en ac c es s to p r ivate, u s er d ep loy ed , ac c es sp oin t s is a p r om is in g c an d id ate for p r ovis ion in g of h ig hd ata r ates in p u b lic w ir eles s ac c es s s y s tem s . T h is d ep loy -m en t s t r ateg y s h ou ld es p ec ially b e an effi c ien t m et h od tos er ve in d oor u s er s in c it ies w it h a s ig n ifi c an t p en etr ation offi x ed b r oad b an d n et w or k s . We es tim ate th e in fr as t r u c t u r ec os t for d iffer en t m ix es of op er ator d ep loy ed ( H S D P Am ac r o c ells an d 8 0 2 .1 1 g WL AN ) an d u s er d ep loy ed ac c es sp oin t s . T h e n etw or k is d im en s ion ed ac c or d in g to a s toc h as -t ic ( log - n or m ally d is t r ib u ted ) m od el for t r affi c d en s it y .R es u lt s in d ic ate th at, w it h t h e m od ellin g an d as s u m p t ion su s ed , u s er d ep loy ed ac c es s p oin t s low er t h e in fr as t r u c t u r ec os t for t r affi c d en s it ies ab ove 1 0 M b p s /k m 2 ( 1 0 t im es t h et r affi c of ty p ic al p r ivate s p eec h u s er s in a c it y c en ter ) .M or eover , on ly a few p er c en t of th e s u b s c r ib er s n eed toin s tall op en ac c es s p oin t s to c over a s ig n ifi c an t fr ac t ion ofth e total t r affi c ter m in ated in d oor s .

I. INTRODUCTION

An in c reas in g availability of fi x ed broad ban d n etwork s ,

in c lu d in g d igital s u bs c r iber lin es an d c able m od em s ,

an d th e d evelop m en t of Wireles s L AN tec h n ology will

en able n ew d es ign s of p u blic wireles s ac c es s n etwork s .

In th is s tu d y th e ec on om ic s of u s er d ep loyed loc al ac c es s

p oin ts (AP s ) th at are als o op en for oth er s u bs c r ibers

an d roam in g p artn er s , is c on s id ered . M ore s p ec ifi c ally,

we will es tim ate th e in f ras tr u c tu re c os t as a fu n c tion

of traffi c d en s ity (area c ap ac ity) for d ifferen t m ix es of

op erator d ep loyed bas e s tation s ( B S ) an d u s er d ep loyed

AP s . Fu r th er m ore, th e n u m ber of AP s n eed ed for s om e

frac tion of c overed traffi c is ad d res s ed .

For th is p u r p os e a tec h n o-ec on om ic al m od el th at

ac c ou n ts for both c ap ital an d op eration al ex p en d itu res

( C AP E X an d O P E X ) is ap p lied . Th e n etwork is d i-

m en s ion ed ac c ord in g to average c ap ac ity an d ran ge p er

ac c es s p oin t an d a s tatis tic al m od el for h eterogen eou s

traffi c d en s ity is u s ed to c ap tu re geograp h ic al variation s

in aggregate (d em an d ed ) th rou gh p u t. An ex am p le of a

n etwork layou t with op erator d ep loyed m ac ro c ellu lar

bas e s tation s an d ran d om ly p lac ed ac c es s p oin ts (AP s )

d ep loyed by u s ers is given in Figu re 1 .

P r ivately own ed AP s th at are op en for p u blic ac c es s

h ave p reviou s ly been p rop os ed in , e.g., [ 1 ,9 ,1 2 ] . In [ 1 2 ]

it was c on c lu d ed th at ap p rox im ately twic e as m an y

AP s are n eed ed to c over in d oor (offi c e) en viron m en ts

with ran d om in s tead of p lan n ed p lac em en t. A s im ilar

c as e is loc al WL AN p rovid ers p res en t in s p ec ifi c areas ,

e.g., air p orts an d h otels . Th es e op erators typ ic ally h ave

roam in g agreem en ts with p u blic op erators wh o ac tu ally

c h arge th e en d u s er s an d in tu r n p ay th es e loc al n etwork

p rovid ers for p rovid in g ac c es s to th eir c u s tom ers [ 1 0 ] .

A c learin g h ou s e m ay als o be u s ed as an in ter m ed iator

Fig . 1 . An ex am p le o f a m ix ed n etw o r k w ith o p erato r d ep lo y ed m ac r oc ells an d u s er d ep lo y ed ac c es s p o in ts .

to h an d le s ettlem en ts between loc al op erators an d th e

u s ers ’ h om e op erators [ 1 3 ] . In th is p ap er we will f u r th er

in ves tigate h ow u s er d ep loyed in f ras tr u c tu re c ou ld be

ex p loited by n etwork op erators th rou gh a fran c h is in g

bu s in es s m od el, referred to as network fra nc h is ing .

Network f ran c h is in g an d th e AP c on c ep t is d es c r ibed

f u r th er in S ec tion II. A tec h n o-ec on om ic al m od el u s ed

for es tim atin g in f ras tr u c tu re c os ts is d es c r ibed in S ec tion

III, an d n u m er ic al res u lts are p res en ted in S ec tion IV .

C on c lu s ion s an d id eas for f u r th er res earc h in th is area

are given in S ec tion V .

II. NETWORK FRANCHISING AND USER DEPLOYED

ACCESS POINTS

It is well k n own th at th e ran ge for h igh d ata rate s ervic es

is lim ited in a c on ven tion al c ellu lar s ys tem an d th is u lti-

m ately lead s to a h igh n u m ber of AP s if an y s ign ifi c an t

area c overage is to be p rovid ed . With c u r ren t tec h n ology

ou td oor bas e s tation s h ave p roblem s in p rovid in g th e

rec eived p ower level req u ired for in d oor c overage of

h igh d ata rates . We c an th erefore as s u m e th at s u c h a

s ys tem , c on s is tin g of a large n u m ber of AP s , m os t lik ely

wou ld be too ex p en s ive to bu ild in a c en traliz ed way

an d th is h as been id en tifi ed as a c h allen ge for f u tu re

p rovis ion in g of wireles s broad ban d s ervic es [ 1 4 ] . O th er

au th ors h ave als o id en tifi ed th e n ec es s ary s h ort ran ge for

h igh d ata rate wireles s ac c es s in th e c on tex t of ” fou r th

gen eration ” m obile n etwork s [ 8 ] .

To res olve th is in tr ic ate is s u e we en vis age a d ec en -

traliz ed bu s in es s m od el wh ere u s ers , or c om p an ies with

loc al p res en c e, in s tall AP s an d c on n ec t th em to ex is tin g

fi x ed broad ban d n etwork s . For n etwork p rovid ers , f ran -

c h is in g p rovid es an op p ortu n ity to lower in s tallation an d

106 Chapter 8. User Deployed Access Points (Paper 4)

operational c osts. Th is oc c u rs bec au se th e u sers d eploy

AP s th em selves in th eir own fac ilities and u se th eir own

power and th eir ex isting fi x ed broad band c onnec tion.

Th ere are natu rally c osts for th e u ser to install th e AP , to

g ive it spac e, for elec tric power, m aintenanc e, and for th e

broad band c onnec tion. H owever, we ex pec t th at u sers

are d eploying a private WL AN ac c ess point anyway, so

th ese c osts will be h id d en for th e u ser and , th erefore,

rem oved from th e operator.

A. T h e n e tw o r k fr a n c h is in g bu s in e s s m o d e l

G enerally speaking , franc h ising refers to a two-layered

bu siness m od el wh ere a franc h iser offers brand nam e and

c ore fu nc tions su c h as proc u rem ent, bac k-offi c e, and IT

su pport to a larg e nu m ber of affi liates, c alled franc h isees.

Th ese franc h isees norm ally h ave to pay a fee for u sing

th e brand and su pport fu nc tionality and follow ru les for,

e.g ., store selec tion, servic e and prod u c t q u alities set u p

by th e franc h iser. H owever, m ost loc al profi ts are kept

by th e loc al franc h isee. Im plem entations of th is bu siness

m od el c an be fou nd in, e.g ., th e retail ind u stry.

With network franc h ising both parties benefi t from th e

arrang em ent. Th e operator obtains ac c essibility to AP s

provid ing c h eap, h ig h -c apac ity wireless ac c ess wh ereas

th e u ser g ets an AP and som e c om pensation by th e

operator. In prac tic e we ex pec t th at operators will c om -

pensate th e AP owner th rou g h bu nd ling of d ifferent

servic es, su c h as fi x ed broad band , su bsid iz ed ac c ess

box es, and wireless ac c ess wh en th e u ser is in oth er

loc ations. N atu rally th e ac tu al d esig n of th e offering will

d epend on th e types of provid ers, wh ic h allows for a

nu m ber of new ways of pac kag ing and d istribu ting th e

d eploym ent; th is issu e is ou tsid e th e sc ope of th is paper.

Th is bu siness m od el c ou ld be of interest to m obile

network operators with lim ited spec tru m and /or poor

ind oor c overag e, or broad band provid ers th at wou ld like

to ex ploit th eir fi x ed network by offering wireless ac c ess

(ind oors). A prom ising bu siness c ase wou ld be oper-

ators th at provid e fi x ed broad band ac c ess and m obile

su bsc riptions as a M obile Virtu al N etwork O perator’s

(M VN O ) . Th ese M VN O s c ou ld offer ind oor and loc al

c overag e by m eans of franc h ising of AP s, th ereby only

u tiliz ing th e m obile network as a c om plem ent (prim arily

for ou td oor and m obile u sers). N ote, h owever, th at

with ou t reg u latory interventions operators with ou t th eir

own fi x ed broad band ac c ess wou ld probably su ffer from

h ig h inter-c onnec tion fees for th e ’last m ile’. In any c ase,

th e proposed bu siness m od el fosters m ore c om petition in

ac c ess networks, wh ic h h as been an im portant objec tive

of telec om reg u lation au th orities for a long tim e [2 ] .

B . U s e r d e p lo y e d a c c e s s p o in ts

U ser d eployed AP s c an su pport a sing le or m u ltiple rad io

ac c ess tec h nolog ies. M oreover we assu m e th at th e AP s

will, wh en installed , be self-c onfi g u ring and au tom at-

ic ally integ rate itself into th e operator’s network (th e

spec ifi c network arc h itec tu re is h owever not with in th e

sc ope of th is stu d y). F u rth erm ore, th e U niversal M obile

Ac c ess (U M A) tec h nolog y c u rrently being d eveloped

will allow u sers with m u lti-m od e h and sets to ac c ess

m obile servic es also via WL AN [1 1 ] . Th is c ou ld be an

im portant step in im plem enting th e proposed bu siness

m od el in prac tic e. In fac t, in th e following we will

assu m e th at eac h AP h as sim ilar c h arac teristic s as IE E E

8 0 2 .1 1 b.

III. SYSTEM MODELLING, ASSUMPTIONS, AND

PERFORMANCE MEASURES

A tec h no-ec onom ic al m od el previou sly presented in [3 ]

will be u sed to estim ate th e infrastru c tu re c ost for a m o-

bile network. Th e m od el is, for th e sake of c onvenienc e,

d esc ribed briefl y nex t.

A. N e tw o r k d im e n s io n in g a n d tr a ffi c m o d e llin g

A h eu ristic m eth od is u sed to d im ension th e rad io ac c ess

network. In operator d eployed system s m ac ro c ells are

d eployed fi rst on an h ex ag onal g rid and d im ensioned

ac c ord ing to traffi c d em and . If th e traffi c ex c eed s th e

m ax im u m c apac ity in som e c ell, WL AN ac c ess points

are d eployed wh ere need ed . Th ese are plac ed in areas

with h ig h est traffi c ( i.e., prim arily in h ot spots). In th e

m ix ed c ase with both u ser- and operator-d eployed base

stations, th e AP s are fi rst d eployed rand om ly ac c ord ing

to a 2 D -P oisson proc ess (i.e., th ey are u niform ly d is-

tribu ted ) . R esid u al traffi c th at c an not be c overed by u ser

d eployed AP s are th en alloc ated to operator d eployed

base stations (ag ain, with m ac ro c ells fi rst) .

Traffi c d ensity is m od elled with a log -norm al, spa-

tially c orrelated , stoc h astic variable over th e servic e

area (1 0 x 1 0 km ) wh ic h is fu rth er d ivid ed into sam ples

of 2 0 x 2 0 m ; see th e ex am ple in F ig u re 2 . A stand ard

d eviation of 7 d B h as been assu m ed and th e c orrelation

d istanc e is 5 0 0 m [ 3 ] . In all nu m eric al ex am ples th e

averag e popu lation d ensity is 2 0 0 0 0 inh abitants/km 2,

c orrespond ing to a c ity c enter. Th e operator is assu m ed

to h ave a 3 0 % m arket sh are and servic e penetration

is 9 0 % . H enc e, th e nu m ber of su bsc ribers is 5 4 0 0

u sers/km 2 on averag e (loc ally th is is m u c h h ig h er) .

B . Ac c e s s p o in t p e r fo r m a n c e a n d c o s t a s s u m p tio n s

AP s are c h arac teriz ed by d ifferent c ell rad ii, c apac ities,

and c osts; see Table I. Th e c apac ity c oeffi c ients for

0 2 000 4 000 6 000 8 000 1 00000

1 000

2 000

3 000

4 000

5 000

6 000

7 000

8 000

9 000

1 0000

Traf

fic D

ensi

ty [l

og10

(Mbp

s/km

2 )]

1

1.5

2

2.5

3

3.5

4

4.5

Fig . 2 . Ex am p le o f traffi c d e n s ity g e n e rate d w ith th e h e te r o g e n e o u straffi c m o d e l f o r a s e r v ic e ar e a o f 1 0 x 1 0 k m .

107

802.11g as s u m es n o c o-c h an n el in terferen c e wh ereas

H S D P A d oes , d u e to th e c ellu lar d ep loym en t an d lim ited

freq u en c y s p ec tr u m ; h ere we as s u m e 3 c arr ier s x 5M H z

(15M H z in total) for d own lin k . N otic e th at th e m axim u m

c ap ac ity for H S D P A an d 802.11g is s im ilar, 22.5 an d

22M bp s , s o th e AP with lowes t c os t p er tran s m itted bit

is es s en tially d eterm in ed by th e geograp h ic al d is tr ibu tion

of traffi c . Th e c ell rad iu s of H S D P A is 1000m for

traffi c d en s ities below 5M bp s /k m 2, 800m between 5 an d

20M bp s /k m 2, an d 4 00m for d en s ities above th at [ 6 ] .

TAB L E I

ACCESS POINT CHARACTERISTICS.

HSDPA 8 0 2 .1 1 g AP

R a d iu s 2 0 0 -10 0 0 m 4 0 m 5 0 m

C a p a c it y [ 3 - 9 ] x 2 .5 Mb p s 2 2 Mb p s 5 Mb p s

C o s t c o e f fi c ie n t 1 (5 5 % /4 5 % ) 0 .13 ( 3 % /9 7 % ) 0 .0 0 6 7( C APE X /O PE X ) + 0 .0 3 p e r c e ll

C os t c oeffi c ien ts in c lu d e both C AP E X an d O P E X ,

wh ere O P E X is c alc u lated in p res en t valu e over a 10-

year p eriod u s in g a 10% d is c ou n t rate (s ee fu r th er [ 4 ] ) .

For th e s ak e of s im p lic ity th e n etwork is d im en s ion ed

to c arry th e s am e traffi c d u r in g th e n etwork life s p an .

Th e c os t for H S D P A is bas ed on th e es tim ate d erived in

[ 4 ] for m ac ro c ell W C D M A bas e s tation s , wh ic h in tu r n

was bas ed on es tim ates p rovid ed by th e G artn er G rou p

an d [ 7 ] . In th e n u m er ic al exam p les we h ave as s u m ed

th at a m ac ro B S c os ts e300k . C os ts for rad io n etwork

c on trollers an d elec tr ic al p ower h ave been included as

op p os ed to th e es tim ates in [ 4 ] . Table I s u m m ar iz es

th e c os t s tr u c tu re in ter m s of C AP E X an d O P E X an d

th e ad d ition al c os t for extra c ells ( d efi n ed as a c arr ier

freq u en c y an d s ec tor) in H S D P A. An in c u m ben t op erator

th at alread y h as s ites for legac y s ys tem s in s talled m ay

reu s e m os t of th es e s ites an d we as s u m e th at th is lowers

th e c os t for H S D P A B S s by 25% .

For 802.11g n ew c os t es tim ates h ave been d ed u c ted

bas ed on [ 7 ] . U s er d ep loyed AP s are as s u m ed to c os t

e2000 in total, in c lu d in g c os ts for eq u ip m en t, c u s tom er

c are, etc ., an d s om e reven u e s h arin g with th e AP own er.

N otic e th at th is is bas ed on ou r as s u m p tion s an d n ot on

em p ir ic al d ata, s in c e th e c on c ep t h as n ot been im p le-

m en ted in p rac tic e yet.

C. In fra s tru c tu re c o s t m ea s u res

Th e bas ic m eas u re for c os t effi c ien c y u s ed is th e in fra s -

tru c tu re c o s t p er G B p er m o n th . In d oin g th is m ap p in g

we as s u m e th at 0.6 % of th e m on th ly traffi c is c ar r ied

d u r in g eac h bu s y h ou r, wh ic h rou gh ly c orres p on d s to

th e traffi c p attern in c u r ren t c ellu lar s ys tem s , an d th at th e

n etwork is d im en s ion ed ac c ord in g to average aggregate

th rou gh p u t ( p er s am p le area). As a referen c e c as e we

will u s e th e traffi c of a typ ic al p r ivate voic e telep h on y

u s er, gen eratin g 20m E of traffi c at 10k bp s d u r in g bu s y

h ou r. Th is c orres p on d s to 200bp s th rou gh p u t on average

d u r in g th e bu s y h ou r.

In th is m od el th e ac tu al traffi c m ix, m obility, etc ., are

th u s exogen ou s . Yet, th e res u lts an d c on c lu s ion s s h ou ld

h old for all traffi c m ixes th at fall with in th e p erform an c e

p aram eters given in Table I.

0 5 1 0 1 5 2 0 2 5 3 0 3 5 4 01 0

2 0

3 0

4 0

50

6 0

7 0

8 0

9 0

1 00

P e rc e n ta g e o f s u b s c rib e rs w ith A P s

Per

cent

age

of tr

affic

cov

ered

2 0m E v o ic e tra ffic1 00 x v o ic e2 00 x v o ic e

T y p ic a l fra c tio n o f tra ffic te rm in a te d in d o o rs in to d a y 's m o b ile s y s te m s

Fig . 3 . P e r c e n tag e o f traffi c c o ve r e d as a fu n c tio n o f th e p e r c e n t o fu s e r s w ith an o p e n AP , f o r d iff e r e n t d ata vo lu m e s .

IV. INFRASTRUCTURE COST ESTIMATES

In th is s ec tion a few n u m eric al exam p les of th e c os t of a

m obile in fras tr u c tu re with an d with ou t AP s are p rovid ed ,

u s in g th e m od els ou tlin ed above.

A . N u m eric a l res u lts

Figu re 3 s h ows th e p erc en tage of traffi c c overed with

AP s as a fu n c tion of th e p erc en tage of s u bs c r ibers

eq u ip p ed with AP s . For low traffi c d en s ities , h ere u p

to ap p roxim ately 100 x voic e traffi c , th e AP s h ave

overc ap ac ity an d th e p lot ac tu ally c orres p on d s to th e

frac tion of th e s ervic e area c overed . As traffi c p er u s er

in c reas es , th e frac tion of traffi c s erved by AP s at a

given AP p en etration ( d en s ity) d ec reas es . B as ed on th es e

res u lts we will as s u m e th at 1, 2, or 4 % of th e u s ers

h ave AP s in th e followin g exam p les . Th is im p lies th at

ap p roxim ately 30, 4 0, an d 7 0% of th e total traffi c is

c overed by AP s . In p rac tic e, it is u n lik ely th at all traffi c

c an be rou ted via u s er d ep loyed AP s s in c e fu ll ou td oor

c overage (in c lu d in g m obile u s ers ) c an n ot be exp ec ted .

A c om m on as s u m p tion tod ay is th at arou n d 80-9 0% of

th e traffi c in m obile n etwork s is ter m in ated in d oors s o

th is c ou ld be s een as an u p p er lim it (as d ep ic ted in 3).

Th e total in fras tr u c tu re c os t for m u lti-ac c es s n etwork s

c on s is tin g of op erator d ep loyed H S D P A an d 802.11g

ac c es s p oin ts in c om bin ation with AP s in s talled by n on e,

1% , 2% , an d 4 % of th e u s ers is d ep ic ted in Figu re 4 .

Th e p lot s h ows th e in fras tr u c tu re c os t p er s u bs c r iber

as a fu n c tion of average traffi c d en s ity. At low traffi c

volu m es an op erator d ep loyed n etwork yield s th e lowes t

c os t, wh ereas for h igh er traffi c in tegratin g AP s in th e

n etwork d ec reas es th e average c os t p er tran s m itted G B .

Th e s lop e of th e c u rves an d c ros s -over p oin ts n atu rally

d ep en d on th e as s u m p tion s on p erform an c e, c os ts , an d

reven u e s h arin g.

Th e n u m ber am ou n t of ac c es s p oin ts an d H S D P A

tran s c eivers (TR X s ) is s u m m ar iz ed in Table II for s everal

levels of AP in s tallation s an d two volu m es of traffi c . For

exam p le at 10 x voic e traffi c , in trod u c in g 100 AP s /k m 2

will yield ap p roxim ately th e s am e c os t as an op erator

d ep loyed n etwork . H owever, th e n u m ber of H S D P A

108 Chapter 8. User Deployed Access Points (Paper 4)

100 101 102

100

101

102

T ra ffic D e n s ity [M b p s /k m 2]

Mon

thly

Infra

stru

ctur

e C

ost [

Eur

o/G

B]

H S + 11gH S + 11g + A P 1%H S + 11g + A P 2%H S + 11g + A P 4 %

Fig. 4 . Mo n th ly c o st p er tran sm itted GB as a fu n c tio n o f averagetraffi c d en sity . Th e so lid lin e is f o r an o p erato r d ep lo y ed m u lti-ac c essn etwo rk with H SDPA an d 8 0 2 .1 1 g. Th e o th er lin es rep resen t d ifferen tp er c en tages o f su b sc r ib ers with APs.

transceivers and op erator d ep loyed 8 0 2 .1 1 g access p oints

are d ecreased sig nifi cantly. At a traffi c of 1 0 0 x voice

th is is even clearer and in th is case th e network of u ser

d ep loyed AP s also bring s a lower cost, as can be seen

in Fig u re 4 .

B. D is c u s s io n o f re s u lts

S ince m any strateg ic factors h ave been left ou t (e.g .

d em and , Q oS , and p reviou s assets of th e op erator) in

th is stu d y we can not m ak e g eneral recom m end ations on

” op tim u m ” d ep loym ent strateg ies. Yet, som e interesting

asp ects can be h ig h lig h ted from a cost p ersp ective.

First, th e econom ics of scale is interesting to note.

I .e., th at increasing traffi c volu m es h as a d im inish ing

increm ental cost. Th is h as p reviou sly been q u estioned

for cellu lar network s and u sed as an arg u m ent for novel

bu siness m od els and d ep loym ent strateg ies su ch as u ser

d ep loyed infrastru ctu re [1 ] , [ 1 4 ] . Th ese resu lts ind icate

th at h ig h er d ata rates can actu ally be p rovid ed at a

sig nifi cantly lower increm ental cost by m eans of an

u ser d ep loyed infrastru ctu re. Th is occu rs even with a

sig nifi cant p ortion of revenu e sh aring (in th is exam p le

ap p roxim ately e1 0 0 p er year p er AP – wh ich in p ractice

is q u ite a su bstantial su bsid y). S econd ly, th e sh are of

investm ents relative to ru nning costs (CAP E X d ivid ed

by O P E X ) is m u ch lower th an for a conventional cel-

lu lar network . Th is typ ically lowers th e risk and is a

virtu e of u ser d ep loyed wireless infrastru ctu re th at is an

interesting top ic for fu rth er research .

V. CONCLUSIONS

Th e infrastru ctu re cost h as been estim ated for d ifferent

d ensities of op erator and u ser d ep loyed access p oints.

Resu lts ind icate th at it is su ffi cient if a few p ercent of th e

su bscribers of a m ajor op erator (with 3 0 % m ark et sh are)

in an u rban area install op en access p oints to lower th e

infrastru ctu re cost sig nifi cantly. With th e u sed m od elling

and assu m p tions, cost saving s are sig nifi cant at traffi c

levels above 1 0 M bp s/k m 2. Th is wou ld ap p roxim ately

corresp ond to ten tim es th e traffi c of p rivate voice u sers.

TAB LE II

THE NUMBER OF TRANSCEIVERS AND ACCESS POINTS REQUIRED

PER SERVED KM2

IN A CITY CENTER.

Subscribers w it h AP 0 % 1 % 2 % 4 %

10 x vo ic e tr a ffi c

H SD P A T R X s 3 .2 2 .6 2 .2 1 .4

8 0 2 .1 1 g AP s 9 .1 3 .6 1 .8 0 .0

AP s 0 .0 5 4 1 1 0 2 2 0

100 x vo ic e tr a ffi c

H SD P A T R X s 2 3 1 9 1 6 1 1

8 0 2 .1 1 g AP s 2 8 1 7 1 0 3 .7

AP s 0 .0 5 4 1 1 0 2 2 0

To com p ensate th e access p oint owners for allowing

p u blic access to th eir internet connections we h ave

assu m ed a su bstantial revenu e sh aring (in th e ord er of

e1 0 0 p er year p er access p oint) . Th u s, introd u cing u ser

d ep loyed access p oints th at are op en for access also to

oth er su bscribers seem s to be a cost effi cient m eth od to

p rovid e h ig h d ata rates in p u blic wireless access system s.

ACKNOWLEDGMENT

Th is work was sp onsored by th e S wed ish Fou nd ation for

S trateg ic Research via th e Afford able Wireless S ervices

and Infrastru ctu re (AWS I) P rog ram and th e L ow Cost

Infrastru ctu re (L CI) P roject. Th e au th or wou ld also lik e

to ack nowled g e th e contribu tions of D r. And ers Fu ru sk ar

( E ricsson Research ) and J onas L ind ( S tock h olm S ch ool

of E conom ics) to th is work .

REFERENCES

[1 ] A. B ria et al., ” 4 th -Gen eratio n Wireless In f rastr u c tu res: Sc en ar-io s an d Researc h Ch allen ges” , IEEE Pe rs on a l C om m u n ic a tion s ,Dec . 2 0 0 1 .

[ 2 ] M. Cave, S. Maju m d ar, an d I. Vo gelsan g (ed ito rs) , ” H an d b o o ko f Telec o m m u n ic atio n s Ec o n o m ic s” , Elsevier Sc ien c e, 2 0 0 2 .

[ 3 ] A. Fu r u sk ar, M. Alm gren , an d K. J o h an sso n , ” An In f rastr u c tu reCo st Evalu atio n o f Sin gle- an d Mu lti-Ac c ess Netwo rk s withH etero gen eo u s User B eh avio r ” , in Proc . IEEE V T C S p rin g 2 0 0 5 .

[ 4 ] K. J o h an sso n , A. Fu ru sk ar, P. Karlsso n , an d J . Z an d er, ” Relatio nb etween b ase statio n c h arac teristic s an d c o st str u c tu re in c ellu larn etwo rk s” , in Proc . IEEE PIM R C 2 0 0 4 .

[ 5 ] K. J o h an sso n , et al. ” In tegratin g User Dep lo y ed Lo c al Ac c essPo in ts in a Mo b ile Op erato r ’s Netwo rk ” , In Proc . WWR F M e e t-

in g n r. 1 2 , 2 0 0 4 .[ 6 ] K. J o h an sso n an d A. Fu ru sk ar, ” Co st effi c ien t c ap ac ity ex p an sio n

strategies u sin g m u lti-ac c ess n etwo rk s” , In Proc . IEEE V T C

S p rin g 2 0 0 5 .[ 7 ] F. Lo iz illo n et al., ” F in a l re s u lts on s e a m le s s m ob ile IP s e rv ic e

p rov is ion e c on om ic s ” , n o t p u b lish ed , IST-2 0 0 0 -2 5 1 7 2 TONICDeliverab le n u m b er 1 1 , Oc t. 2 0 0 2 .

[ 8 ] W. Mo h r, R. Lu d er, an d K-H . Mo h r m an n , ” Data Rate Estim ates,Ran ge Calc u latio n s an d Sp ec tr u m Dem an d f o r New Elem en ts o fSy stem s B ey o n d IMT-2 0 0 0 ” , in Proc . IEEE WPM C 2 0 0 2 .

[ 9 ] J . M. Pereira, ” Fo u r th Gen eratio n : No w it is Perso n al! ” , in Proc .

IEEE PIM R C 2 0 0 0 .[ 1 0 ] O. Tirla et al., ” Ac c o u n tin g m an agem en t in h etero gen eo u s

m o b ile ac c ess n etwo rk s: th e MIND ap p r o ac h ” , in Proc . IEEE

WPM C 2 0 0 2 .[ 1 1 ] UMA Tec h n o lo gy , h ttp ://www.u m atec h n o lo gy .o rg/[ 1 2 ] Matth ias Un b eh au n , ” O n th e D e s ig n a n d D e p loy m e n t of L ow -

c os t Wire le s s In fra s tru c tu re ” , Do c to ral Dissertatio n , Ro y al In sti-tu te o f Tec h n o lo gy , Dep artm en t o f Sign als, Sen so rs an d Sy stem s,2 0 0 2 .

[ 1 3 ] S. Wo lters an d H . Lu ed iger, ” Reso u r c e b r o k erage in f u tu rewired an d wireless n etwo rk s - WH Y LESS.COM” , n o t p u b lish ed ,availab le at h ttp ://www.wh y less.o rg/p u b lic /wp 3 .h tm .

[ 1 4 ] J . Z an d er, ” On th e c o st str u c tu re o f f u tu re wid eb an d wirelessac c ess” , in Proc . IEEE V T C 1 9 9 7 .

Chapter 9

Radio ResourceManagement in RoamingBased Multi-OperatorWCDMA networks (Paper5)

Klas Johansson, Martin Kristensson, and Uwe Schwarz,In Proc. IEEE VTC2004 Spring, May 2004.

109

111

Radio Resource Management in Roaming Based

Multi- O p erator W C D MA N etw ork s

K las J oh ansson

Radio C ommunication Sy stems L ab oratory

D ep t. of Signals, Sensors & Sy stems

Roy al Institute of T ech nology

S-1 0 0 4 4 ST O C K H O L M, Sw eden

E mail: k lasj@ radio.k th .se

Martin K ristensson and U w e Sch w arz

N ok ia N etw ork s

Saterinp ortti, P L 3 0 1

0 0 0 4 5 N ok ia G roup , F I N L A N D

E mail: {martin.k ristensson, uw e.sch w arz }@ nok ia.com

Abstract— Infrastructure sharing and Mobile Virtual NetworkO p erators (MVNO ) are becom ing m ore and m ore com m on intoday s m obile networks. In both cases the subscribers of m ultip leop erators connect to the sam e radio access network v ia, e.g.,roam ing based m ethods. D ep ending on how m uch each op eratorp ay s, they should then be guaranteed a certain cap acity in theshared network.

T his p ap er discusses different solutions for how the radioresources in such a roam ing based m ulti-op erator W C D MA net-work m ay be allocated to the sharing op erators. O ne p articularm ethod based on R adio R esource Managem ent (R R M) with non-p reem p tiv e p riority q ueuing in the adm ission control is p resentedin detail. T he m ethod seem s to p rov ide an attractiv e tradeoffbetween fairness and total sy stem cap acity .

I . I N T RO D U C T I O N

Sh aring th e radio access netw ork (RA N ) h as b ecome p op -

ular among U MT S mob ile op erators during th e recent y ears.

T h e main reason is p erh ap s to low er th e inv estment costs, b ut

it could also b e to reduce op erating costs in th e long run.

In p articular in rural areas, w h ere cov erage driv es th e total

netw ork cost, th is h as also to some ex tent b een imp lemented

in p ractice. Moreov er, op erators w ith out a 3 G license may act

as Mob ile V irtual N etw ork op erators, and th ereb y ex tend th ere

serv ice offering b y roaming into anoth er op erators netw ork .

F or th is p urp ose, similar functionality as w ith interna-

tional roaming can b e utiliz ed to inter-connect th e op erators

netw ork s. A nd, th is p ap er inv estigates meth ods for h ow to

allocate radio resources in such roaming b ased multi-op erator

W C D MA netw ork s.

T ech nically , w ith roaming based sharing, an op erator access

anoth er op erators RA N indirectly v ia th e core netw ork s. T h is

imp lies th at multip le op erators fully sh are th e same RA N ,

and th ere is h ence a p otential need for radio resource control

b etw een th e op erators. N ormally th e op erators sh are th e same

carrier(s) , b ut it is also p ossib le to use dedicated carriers.

Besides roaming b ased sh aring, w h ich is th e top ic of

th is p ap er, th ere are also oth er sh aring solutions. T h e main

categories th at w e see today are R A N sharing and site sharing.

T h e th ree group s of solutions imp ly different lev els of sh aring,

w h ich is dep icted in F ig. 1 .

W ith RA N b ased sh aring th e op erators h av e dedicated

carriers b ut sh are netw ork elements up to and including th e

Core network

R a d io N etwork Controller

B S B S B S B S

R oa m ing b a s ed

s h a ring

S ite

s h a ring

R A N s h a ring

F ig. 1 . T h e fi gure illustrates w h at lev els of th e U MT S netw ork arch itectureth at different sh aring meth ods relate to.

radio netw ork controller (RN C ) , or p ossib ly only th e b ase

stations. T h e adv antage is th at op erators, alth ough sh aring

signifi cant p arts of th e RA N , still h av e a h igh degree of

indep endence.

T h e isolation b etw een th e sh aring op erators is ev en b etter

w ith site sh aring. In th is case th ey only sh are for ex amp le th e

p lace w h ere th e b ase station is located or p h y sical eq uip ment,

such as for ex amp le antennas and p ow er sup p lies.

W ith th ose infrastructure sh aring meth ods it is p ossib le to

imp lement a numb er of different use cases. F or ex amp le,

• geograp h ical sh aring w h ere op erators p rov ide cov erage

in different p arts of a country , or

• a mix of ow n netw ork s in urb an areas and a common

sh ared netw ork in low p op ulated areas.

Sh ared netw ork s h av e recently b een under inv estigation

in 3 G P P ; see [6 ] and [7 ] . T h ough , RRM algorith ms are

mainly p rop rietary and h ence not treated in 3 G P P . In th e

research community sh ared netw ork s h av e b een discussed in

for ex amp le [1 ] , [ 5 ] and [4 ] , b ut th e RRM asp ects in roaming

b ased sh aring h av e not attracted much research interest y et.

T h e outline of th e p ap er is as follow s. In Section II w e

b riefl y discuss different w ay s for sh aring th e radio resources

b etw een multip le op erators w ith roaming b ased sh aring. T h en,

in Section II I a meth od to dy namically allocate cap acity is

p resented in more detail. T h e p erformance of th is algorith m

is analy z ed w ith simp le simulations in Section IV , and con-

clusions are draw n in Section V .

112 Chapter 9. Fair Resource Sharing (Paper 5)

II. ME T H O D S F O R A L L O C A T IN G R A D IO R E S O U R C E S W IT H

R O A MIN G B A S E D S H A R IN G

H o w m u c h o f th e r a d io n e tw o r k c a p a c ity th a t e a c h s h a r in g

p a r tn e r h a s th e r ig h t to u s e w ith r o a m in g b a s e d s h a r in g is

c o m m o n ly s p e c ifi e d in a S e r v ic e L e v e l A g r e e m e n t ( S L A ) . A n

o p e r a to r th a t f o llo w s its te r m s in th e S L A s h o u ld r e c e iv e th e

a g r e e d Q u a lity o f S e r v ic e ( Q o S ) le v e ls ; th is e v e n if th e o th e r

o p e r a to r s tr y to u tiliz e m o r e c a p a c ity th a n a g r e e d . T h e o n ly

w a y f o r a n o p e r a to r to o b ta in m o r e c a p a c ity s h o u ld th u s b e to

e ith e r p a y f o r a la rg e r s h a r e , o r in v e s t in m o r e c a p a c ity .

In p r a c tic e th is m e a n s th e th e r a d io r e s o u r c e m u s t b e s h a r e d

in a c o n tr o lle d w a y b e tw e e n th e o p e r a to r s . A n d , th e r a d io

r e s o u r c e s c a n in p r in c ip le b e a llo c a te d b y :

• u s in g d e d ic a te d c a r r ie r s f o r e a c h o p e r a to r,

• a llo c a tin g a fi x e d c a p a c ity s h a r e f o r e a c h o p e r a to r p e r

c a r r ie r, o r

• d y n a m ic a lly p r io r itiz e o p e r a to r s ( w ith in o n e o r m u ltip le

c a r r ie r s ) .

N e x t w e w ill d is c r ib e th o s e m e th o d s b r ie fl y , a n d d is c u s s th e

a p p lic a b ility f o r d iff e r e n t u s e c a s e s .

A. Dedicated carriers

In th is c a s e th e o p e r a to r s s h a r e f o r e x a m p le th e b a s e s ta tio n s ,

th e tr a n s m is s io n n e tw o r k a n d th e r a d io n e tw o r k c o n tr o lle r, b u t

th e y e a c h h a v e th e ir d e d ic a te d c a r r ie r la y e r.

D e d ic a te d c a r r ie r s r e s u lt in g o o d in te r- o p e r a to r is o la tio n ,

b u t th e d e d ic a te d c a r r ie r s a ls o r e s u lt in u n n e c e s s a r y h ig h

in v e s tm e n t c o s ts in s o m e s c e n a r io s . E s p e c ia lly in r u r a l a r e a s

th e c a p a c ity o f a s in g le - c a r r ie r W C D MA n e tw o r k c o u ld b e

w e ll e n o u g h to c o v e r th e n e e d s f o r m u ltip le o p e r a to r s .

B . F ix ed cap acity sh ares p er carrier

W ith a d v a n c e d R R M fu n c tio n a lity , a fi x e d f r a c tio n o f th e

c e ll c a p a c ity c a n b e r e s e r v e d f o r e a c h o p e r a to r a n d o n ly o n e

c a r r ie r is th u s r e q u ir e d . T h is a p p r o a c h r e s u lts in lo w e r in v e s t-

m e n t c o s ts a s c o m p a r e d to d e d ic a te d c a r r ie r s ( in p a r tic u la r in

c o v e r a g e lim ite d a r e a s ) a n d it s till p r o v id e s p e r f e c t fa ir s h a r in g

o f th e a v a ila b le c a p a c ity . H o w e v e r, it c o u ld a ls o le a d to a lo s s

in to ta l s y s te m c a p a c ity a s c o m p a r e d to f u lly s h a r e d c a r r ie r s

d u e to a d e c r e a s e d s ta tis tic a l m u ltip le x in g g a in .

A n e x a m p le o f th e tr u n k in g e ffi c ie n c y is g iv e n in F ig . 2 .

H e r e th e a v e r a g e c h a n n e l u tiliz a tio n η is d e p ic te d a s a f u n c tio n

o f th e n u m b e r o f a v a ila b le c h a n n e ls p e r c e ll C. F o r a to le r a b le

b lo c k in g p r o b a b ility Bmax = 5% ( c a lc u la te d a c c o r d in g to

th e w e ll k n o w n E r la n g - B f o r m u la , s e e e .g . [ 3 ] ) , th e a v e r a g e

c h a n n e l u tiliz a tio n is h e r e d e fi n e d a s

η =O

C, ( 1 )

w h e r e th e o ff e r e d lo a d O is th e to ta l o ff e r e d lo a d g iv e n in

E r la n g a n d th e n u m b e r o f c h a n n e ls C is a c o n s ta n t.

W ith C = 8 0 c h a n n e ls a v a ila b le p e r c e ll th e to ta l c a p a c ity

is r e d u c e d w ith 1 0 % a lr e a d y w ith tw o o p e r a to r s s h a r in g

th e c a p a c ity in e q u a lly s iz e d s h a r e s . A n d w ith f o u r s h a r in g

o p e r a to r s th e lo s s is u p to 2 0 %. N o tic e th a t th is a ls o b r in g s

m o r e c o s ts w h ic h w o u ld c o n tr a d ic t w ith th e m a in p u r p o s e o f

0 10 2 0 3 0 4 0 5 0 6 0 7 0 8 00.4

0.5

0.6

0.7

0.8

0.9

1

Avera

ge c

hannel utiliz

ation,

η

N u m b e r o f c h a n n e ls p e r c e ll, C

2 0%re d u c tio n

10%re d u c tio n

F ig . 2 . C h a n n e l u tiliz a tio n η a s a f u n c tio n o f th e to ta l n u m b e r o f c h a n n e lsC p e r c e ll w ith 5 % a v e r a g e b lo c k in g p r o b a b ility .

s h a r in g a W C D MA n e tw o r k . T h u s , w ith fi x e d c a p a c ity s h a r e s ,

th e c o s t o p e r a to r s h a v e to p a y f o r th e fa ir c a p a c ity a llo c a tio n

is m o s t lik e ly h ig h e r th a n th e v a lu e a d d e d .

C . Dy n am ical p rio ritiz atio n o f o p erato rs w ith in o n e o r m u l-

tip le carriers

A s th e s o lu tio n s d is c u s s e d a b o v e a ll le a d to a s ig n ifi c a n t

lo s s in to ta l s y s te m c a p a c ity o r c o s t e ffi c ie n c y , a d y n a m ic a l

p r io r itiz a tio n o f o p e r a to r s b a s e d o n th e c u r r e n t lo a d is p r e f e r-

a b le . F o r th is p u r p o s e s ta n d a r d R R M fu n c tio n a lity s u c h a s

a d m is s io n c o n tr o l a n d p a c k e t s c h e d u lin g c a n b e u tiliz e d .

A d m is s io n c o n tr o l is in th is c o n te x t r e s p o n s ib le f o r a d m is -

s io n o f n e w c o n n e c tio n s ( b o th p a c k e t a n d c ir c u it s w itc h e d ) ,

w h e r e a s p a c k e t s c h e d u lin g a d a p tiv e ly a d ju s ts th e b it r a te o f

c o n n e c te d n o n r e a l- tim e b e a r e r s [ 2 ] .

F o r th e s a k e o f s im p lic ity , w e o n ly tr e a t c ir c u it s w itc h e d

tr a ffi c in th is p a p e r. H o w e v e r, it s h o u ld b e s tr a ig h tf o r w a r d

to e x te n d th e s o lu tio n to h a n d le a ls o p a c k e t s w itc h e d tr a ffi c .

Mo re o v e r, o n ly a s in g le - s e r v ic e s y s te m is tr e a te d .

T h e p r io r itiz a tio n c a n im p le m e n te d w ith p r io r ity q u e u in g

in th e a d m is s io n c o n tr o l. E a c h c o n n e c tio n b e lo n g in g to a n

o p e r a to r s h o u ld th e n r e c e iv e a p r io r ity c a lc u la te d b a s e d o n

th a t o p e r a to r s c u r r e n t lo a d r e la tiv e to th e ir a g r e e d m in im u m

c a p a c ity , s o th e p r io r ity le v e l r e fl e c ts h o w m u c h e a c h o p e r a to r

h a s u tiliz e d its a g r e e d c a p a c ity s h a r e ..

A n o th e r d e s ig n d e c is io n is w h e th e r o r n o t to u s e p r e -

e m p tio n ( a llo w in g f o r r e m o v a l o f e x is tin g c o n n e c tio n s to m a k e

s p a c e f o r a n e w r e q u e s t) . P r e e m p tio n w o u ld p e r d e fi n itio n

in c r e a s e th e p r o b a b ility o f d r o p p in g a c tiv e c o n n e c tio n s . In

g e n e r a l th is is c o n s id e r e d to b e th e m o s t im p o r ta n t q u a lity

o f s e r v ic e m e a s u r e f o r c ir c u it- s w itc h e d s e r v ic e s ( lik e v o ic e

te le p h o n y ) a n d p r e e m p tio n is h e n c e n o t p r e f e r a b le .

H a v in g s a id th a t, th e m e th o d w ith n o n - p r e e m p tiv e p r io r ity

q u e u in g in a d m is s io n c o n tr o l s e e m s to b e th e m o s t p r o m is in g

s o lu tio n to th e s ta te d p r o b le m a n d w e w ill f o c u s o n th is m e th o d

in th e s e q u e l o f th is p a p e r.

113

III. MU L T I-O P E R A T O R A D MIS S IO N C O N T R O L W IT H

N O N -P R E E MP T IV E P R IO R IT Y Q U E U E IN G

A. System model and performance measures

F o r th e f u r h e r a n a ly s is a s ta n d a r d P o is s o n tr a ffi c m o d e l w ill

b e u s e d ( s e e e .g . [ 3 ] ) . T h e to ta l o ff e r e d lo a d p e r o p e r a to r i is

d e n o te d Oi, a n d is d e fi n e d a s

Oi = λiT, ( 2 )

w h e r e λi is th e a v e r a g e a r r iv a l r a te o f n e w c o n n e c tio n s f o r

o p e r a to r i a n d T is th e a v e r a g e d u r a tio n p e r c o n n e c tio n . T h e

to ta l o ff e r e d lo a d is th e n g iv e n b y

O =

N∑

i=1

Oi, ( 3 )

a s s u m in g th a t N o p e r a to r s s h a r e th e n e tw o r k .

T h e to ta l n u m b e r o f c h a n n e ls p e r c e ll C is s till m o d e le d

a s c o n s ta n t. A lth o u g h it is w e ll k n o w n th a t th is is n o t th e

c a s e in a W C D MA s y s te m ( s e e e .g . [ 2 ] , w e b e lie v e th is in itia l

a s s e s s m e n t is n o t im p r o v e d s ig n ifi c a n tly b y m o d e lin g th is in

m o r e d e ta il.

E a c h o p e r a to r is a s s ig n e d a m in im u m c a p a c ity le v e l p e r c e ll,

Ci, h e r in c o r r e s p o n d in g to th e n u m b e r o f c h a n n e ls a n o p e r a to r

is g u a r a n te e d a c c o r d in g to th e S L A .

A n e w c o n n e c tio n r e q u e s t is q u e u e d u n til th e r e a r e r e s o u r c e s

a v a ila b le . H o w e v e r, if th e w a itin g tim e Td e x c e e d s a c e r ta in

th r e s h o ld Tm a x th e r e q u e s t is b lo c k e d . T h e b lo c k in g p r o b a b il-

ity f o r a n o p e r a to r, Bi, is th u s d e fi n e d a s

Bi = Pr (Td > Tm a x )×Pr (U s e r b e lo n g s to o p e r a to r i). ( 4 )

F u r th e r m o r e , th e lo a d p e r o p e r a to r Li is s im p ly g iv e n b y

th e to ta l n u m b e r o f a llo c a te d c h a n n e ls f o r th a t o p e r a to r a t a

g iv e n p o in t in tim e .

B . Alg orith m description

A n o v e r v ie w o f th e a lg o r ith m u s e d f o r a d m is s io n c o n tr o l

w ith n o n -p r e e m p tiv e p r io r ity q u e u in g is d e p ic te d in F ig . 3 .

T h e p r io r ity le v e l o f e a c h o p e r a to r, Pi, is d e fi n e d a s

Pi =Ci

Li

, ( 5 )

s o th a t o p e r a to r s w ith a lo a d Li lo w e r th a n th e a g r e e d

m in im u m c a p a c ity Ci r e c e iv e s a h ig h p r io r ity .

T h e q u e u in g m a n a g e m e n t o u tlin e d h e r e c a n b e im p le m e n te d

in c o n ju n c tio n w ith , e .g ., th e p e r io d ic a l a d m is s io n c o n tr o l

f u n c tio n a lity d e s c r ib e d in [ 2 ] a n d it c o n s is ts o f th e f o llo w in g

s te p s .

1 ) A n e w c o n n e c tio n r e q u e s t th a t a r r iv e s w h e n th e s y s te m

is c o n g e s te d is p u t in th e q u e u e .

2 ) T h e q u e u e is p e r io d ic a lly s o r te d in a n d e s c e n d in g o r d e r

a c c o r d in g to Pi. T h e n e a c h o p e r a to r ’s c o n n e c tio n s a r e

s o r te d g r o u p -w is e in a d e s c e n d in g o r d e r b a s e d o n Td.

C o n s e q u e n tly , th e o p e r a to r w ith h ig h e s t Pi w ill b e

s e r v e d fi r s t a n d e a c h o p e r a to r ’s c o n n e c tio n r e q u e s ts a r e

s e r v e d in a fi r s t-in -fi r s t-o u t ( F IF O ) m a n n e r r e la tiv e to

e a c h o th e r.

Put new connection

req ues t(s ) in q ueue

C h a nnel a v a ila b le?

S ort q ueue: 1 ) Pi 2 ) Td

A lloca te ch a nnel C onnection b lock ed!

Td> Tm a x ?

Y es Y es

N o

N o

F ig . 3 . F lo w c h a r t o f p e r io d ic a l a d m is s io n c o n tr o l w ith n o n -p r e e m p tiv ep r io r ity q u e u in g .

3 ) N o r m a l a d m is s io n c o n tr o l is p e r f o r m e d f o r e a c h c o n n e c -

tio n r e q u e s t in th e q u e u e in p r io r itiz e d o r d e r. If e n o u g h

r e s o u r c e s a r e a v a ila b le , a c h a n n e l is a llo c a te d .

4 ) C o n n e c tio n r e q u e s ts f o r w h ic h Td > Tm a x a r e b lo c k e d

a n d r e m o v e d f r o m th e q u e u e .

C . P erformance measures

H o w e ff e c tiv e th e d iff e r e n tia tio n in b lo c k in g p r o b a b ility is

d e p e n d s o n th e p r o b a b ility th a t e n o u g h r e s o u r c e s a r e r e le a s e d

b e f o r e th e m a x im u m a llo w e d w a itin g tim e Tm a x is r e a c h e d

a n d a c o n n e c tio n h a s to b e b lo c k e d . T h is s h o u ld m a in ly b e

a f u n c tio n o f u s e r b it r a te s , a v e r a g e c o n n e c tio n d u r a tio n s a n d

th e m a x im u m a llo w e d q u e u in g tim e .

H e n c e , th e p e r f o r m a n c e c a n b e e v a lu a te d b y o b s e r v in g th e

o p e r a to r s p e c ifi c b lo c k in g p r o b a b ility Bi. If th e a lg o r ith m

p e r f o r m s w e ll,

Bi ≤ Bm a x f o r Oi < Ci. ( 6 )

T h a t is , Bi s h o u ld b e b e lo w a c e r ta in th r e s h o ld Bm a x u n til

th e o p e r a to r r e a c h e s its a g r e e d m in im u m c a p a c ity Ci. A n d

a s a c o n s e q u e n c e , a t h ig h to ta l o ff e r e d lo a d O th e b lo c k in g

p r o b a b ility w ill b e h ig h e r f o r o p e r a to r s ’ th a t h a v e e x c e e d e d

th e ir lo a d s h a r e .

IV . S IMU L A T IO N S A N D R E S U L T S

T h e p e r f o r m a n c e o f th e a lg o r ith m o u tlin e d in S e c tio n III h a s

b e e n in v e s tig a te d b y m e a n s o f s im p le q u e u in g s im u la tio n s w ith

tw o o p e r a to r s (N = 2) . F ir s t, g e n e r a l s im u la tio n a s s u m p tio n s

a n d m o d e ls a r e d e s c r ib e d . T h e n a f e w e x a m p le s a r e g iv e n to

s h o w h o w th e a lg o r ith m c o u ld f u n c tio n in d iff e r e n t s c e n a r io s .

A. G eneral assumptions and traffi c models

A s in g le c e ll h a s b e e n s im u la te d in w h ic h a ll c o n n e c tio n s

h a v e th e s a m e b it r a te . E a c h o p e r a to r h a s p r io r itiz e d a c c e s s to

a to ta l n u m b e r o f Ci c h a n n e ls . A n d , if n o t s ta te d o th e r w is e ,

b o th o p e r a to r s h a v e th e s a m e g u a r a n te e d c a p a c ity s o th a t

C1 = C2 =C

2. ( 7 )

114 Chapter 9. Fair Resource Sharing (Paper 5)

TABLE I

SIM U LATED SER V IC ES

Se rv ic e Sp e e c h V id e o s tr e a m in g

C h a n n e ls p e r c e ll C 8 0 1 6Allo w e d q u e u in g tim e Tmax [ s ] 5 s 1 5 sAv e r a g e c o n n e c tio n tim e 1 2 0 s 1 2 0 sD a ta r a te 1 2 .2 k b p s 6 4 k b p s

A c o n n e c tio n is a d m itte d if th e r e is a t le a s t o n e c h a n n e l

a v a ila b le , th a t is ifN∑

i=1

Li < C, (8 )

a n d b lo c k e d if n o c h a n n e l is r e le a s e d b e f o r e th e m a x im u m

a llo w e d w a itin g tim e Tm a x is e x c e e d e d . Th e p r io r ity le v e l Pi

is u p d a te d c o n tin u o u s ly w ith o u t a n y te m p o r a l a v e r a g in g a n d

is th e s a m e f o r a ll c o n n e c tio n s b e lo n g in g to th e s a m e o p e r a to r.

Th e to ta l n u m b e r o f c h a n n e ls C a n d th e m a x im u m w a itin g

tim e Tm a x a r e s e r v ic e s p e c ifi c a n d th e s a m e in a ll s im u la tio n s ;

s e e Ta b le I.

N o te a ls o th a t, a c c o r d in g to th e P o is s o n tr a ffi c m o d e l

u s e d , b o th th e in te r- a r r iv a l tim e s o f c o n n e c tio n r e q u e s ts a n d

c o n n e c tio n d u r a tio n s a r e e x p o n e n tia lly d is tr ib u te d .

B. Blocking probability for a speech service

Th e b lo c k in g p r o b a b ility o f a n o p e r a to r i = 1 is d e p ic te d

in F ig . 4 a s f u n c tio n o f its o ff e r e d lo a d O1. Th is h a s b e e n

s im u la te d f o r a f e w d iff e r e n t v a lu e s o f o ff e r e d lo a d f o r th e

s e c o n d o p e r a to r, O2. N o w , a c c o r d in g to (6 ) , th e b lo c k in g

p r o b a b ility o f th e s tu d ie d o p e r a to r B1 s h o u ld b e k e p t b e lo w

s o m e th r e s h o ld Bm a x a s lo n g a s L1 < C1.

F ig . 4 s h o w s th a t th e a lg o r ith m p e r f o r m s w e ll f o r a s p e e c h

s e r v ic e . Th is is s im p ly d u e to th a t th e r e a r e q u ite m a n y

c h a n n e ls (8 0 ) a v a ila b le p e r c a r r ie r. Th u s , th e r e is a h ig h

lik e lih o o d th a t a c h a n n e l is r e le a s e d b e f o r e th e m a x im u m

a llo w e d q u e u in g tim e Tm a x is e x c e e d e d .

20 3 0 40 5 0 60 7 0 800

0.05

0.1

0.15

0.2

0.25

0.3

0.3 5

Offe re d lo a d fo r o p e ra to r 1, O1 [E rl]

Blo

ckin

g p

rob

ab

ility

for

opera

tor

1,

B1

F ix e d re s e rv a tio nO

2 = 80 E rl

O2 = 60 E rl

O2 = 40 E rl

O2 = 20 E rl

F ig . 4 . Th e b lo c k in g p r o b a b ility o f o p e r a to r 1 (B1) f o r a s p e e c h s e r v ic e a s af u n c tio n o f o ff e r e d lo a d (O1) f o r d iff e r e n t le v e ls o f lo a d f o r th e o th e r o p e r a to r(O2) . A fi x e d r e s o u r c e a llo c a tio n o f 4 0 c h a n n e ls p e r o p e r a to r is d e p ic te d a sa r e f e r e n c e .

A fi x e d c a p a c ity a llo c a tio n o f 4 0 c h a n n e ls d e d ic a te d f o r

o p e r a to r 1 is d e p ic te d a s a r e f e r e n c e c a s e . In te r e s tin g to n o te

is th a t f o r a lo w lo a d th e b lo c k in g p r o b a b ility is h ig h e r w ith

d y n a m ic a l p r io r itiz a tio n th a n w ith d e d ic a te d c a p a c ity . H o w -

e v e r, a s s o o n a s th e lo a d in c r e a s e th e d y n a m ic a l p r io r itiz a tio n

o u tp e r f o r m s a fi x e d a llo c a tio n o f c h a n n e ls .

Th e a p p r o x im a te g a in in te r m s o f o ff e r e d lo a d f o r a to le r a b le

b lo c k in g p r o b a b ility Bm a x = 5 % is s u m m a r iz e d in Ta b le

II. Th e g a in a s c o m p a r e d to a d e d ic a te d c a p a c ity s c h e m e is

n a tu r a lly h ig h e r w h e n th e to ta l s y s te m lo a d is lo w .

TABLE II

GAIN R ELATIV E TO A F IX ED ALLO C ATIO N O F 4 0 SP EEC H C H AN N ELS W ITH

Bmax = 5% TO LER ABLE BLO C K IN G P R O BABILITY .

O2 [Erl] 8 0 6 0 4 0 2 0O1 [Erl] 4 1 .6 4 4 5 1 7 2

Ga in 5 % 1 0 % 3 0 % 8 0 %

F in a lly , w e c a n a ls o s e e in F ig . 4 th a t, w h e n th e o ff e r e d lo a d

f o r th e s e c o n d o p e r a to r O2 in c r e a s e , s o d o e s u n f o r tu n a te ly

a ls o th e b lo c k in g p r o b a b ility o f o p e r a to r 1 (B1) . H o w e v e r, in

th is c a s e B1 c a n s till b e k e p t b e lo w Bm a x = 5 % . Th u s , w e

c o n c lu d e th e a lg o r ith m p e r f o r m s w e ll f o r a W C D M A s y s te m

w ith s p e e c h u s e r s a n d th is s h o u ld a ls o h o ld f o r a n y c ir c u it

s w itc h e d v id e o a n d d a ta s e r v ic e w ith a m o d e r a te d a ta r a te .

C . Blocking probability for a stream ing vid eo service

As th e b it r a te in c r e a s e , th e to ta l n u m b e r o f c h a n n e ls

a v a ila b le p e r c a r r ie r d e c r e a s e a n d th e n o n p r e m p tiv e p r io r ity

q u e u in g a lg o r ith m s h o u ld c o n s e q u e n tly p e r f o r m w o r s e . Th is

is a ls o in d ic a te d b y th e s im u la te d r e s u lts in F ig . 5 f o r a v id e o

s tr e a m in g s e r v ic e .

In th is c a s e th e b lo c k in g p r o b a b ility o f th e fi r s t o p e r a to r B1

c a n n o t b e k e p t b e lo w Bm a x = 5 % w h e n th e lo a d is h ig h

f o r th e o th e r o p e r a to r, a n d th e r e is h e n c e a lo s s in c a p a c ity

2 4 6 8 10 120

0.05

0.1

0.15

0.2

0.25

Offe re d lo a d fo r o p e ra to r 1, O1 [E rl]

Blo

ckin

g p

robabili

ty for

opera

tor

1, B

1

F ix e d re s e rv a tio nO

2 = 14 E rl

O2 = 10 E rl

O2 = 6 E rl

O2 = 2 E rl

F ig . 5 . Th e b lo c k in g p r o b a b ility o f o p e r a to r 1 (B1) f o r a v id e o s tr e a m in gs e r v ic e a s a f u n c tio n o f o ff e r e d lo a d (O1) f o r d iff e r e n t le v e ls o f lo a d f o r th eo th e r o p e r a to r (O2) . A fi x e d r e s o u r c e a llo c a tio n o f 8 c h a n n e ls p e r o p e r a to ris d e p ic te d a s a r e f e r e n c e .

115

as compared to the reference case with 8 dedicated channels;

see T ab le II I . T his despite the fact that we hav e increased the

max imu m allowed waiting time Tmax to 1 5 s (instead of 5 s

which was u sed for the speech serv ice). H owev er, with a more

moderate load for the second operator, for ex ample O2 = 6

E rl, the alg orithm fu nctions well also in this case.

T AB L E I I I

GAI N RE L AT I V E T O A F I X E D AL L O C AT I O N O F 8 S T RE AM I N G C H AN N E L S

W I T H 5% T O L E RAB L E B L O C K I N G P RO B AB I L I T Y .

O2 [ E rl] 1 4 1 0 6 2O1 [ E rl] 2 .0 4 .6 6 .9 1 1

Gain -6 0 % - 6 % 4 0 % 1 2 0 %

T hese resu lts indicate that for circu it switched serv ices

with hig h data rates, one cou ld consider to slowly adju st the

minimu m capacity g u aranteed per operator Ci according to

the av erag e demand. F or pack et switched serv ices, howev er,

it is simpler to mu ltiplex b etween u sers and the same ty pe of

prob lem shou ld hence not occu r for su ch traffi c.

D. Coexistence of operators with low and high minimum

agreed capacity per cell

In the prev iou s simu lations, b oth operators’ had the same

g u aranteed capacity lev el Ci. I t is also of interest to u nderstand

if an operator with a low capacity share can coex ist with an

operator that has a hig her reserv ed capacity and a larg er share

of the offered traffi c load.

In F ig . 6 we show a few ex amples for a speech serv ice

where C1 = 2 0 and C2 = 60 . T he load for operator 1 , O1,

was increased linearly for two different lev els of O2; namely

6 0 and 1 0 0 E rlang .

F rom these resu lts, it can b e noticed that the b lock ing

prob ab ility can b e k ept low also for operators with a small

fraction of the total capacity . H owev er, the b lock ing prob -

ab ility of the operator with low load is increased slig htly

as the load of the second operator increase. O n the other

hand, B2 � B1, so the b lock ing prob ab ility of the b ig g er

operator is sig nifi cantly hig her. T hu s, the alg orithm clearly

differentiates the b lock ing prob ab ility of the two operators. S o

from a fairness perspectiv e, the alg orithm performs well also

in this case and one operator can not cannib aliz e on the other

operators resou rces withou t a penalty in terms of increased

b lock ing prob ab ility .

V . C O N C L U S I O N

S haring radio resou rces in a mu lti-operator W C D M A net-

work is b est done with a comb ination of serv ice lev el ag ree-

ments (S L A) and su pporting fu nctionality for fair resou rce

sharing . If the operators will ex perience clear drawb ack s

when they v iolate an S L A, they will b e forced to follow the

ag reement, b u y capacity from another sharing partner or pay

for a capacity ex pansion of the shared network .

T his paper ou tlined mu ltiple technical solu tions to the

radio resou rce-sharing prob lem and describ ed one of them,

admission control with non-preemptiv e priority q u eu ing , in

5 10 15 20 25 3 00

0.05

0.1

0.15

0.2

0.25

O ffe re d lo a d fo r o p e ra to r 1, O1 [E rl]

Opera

tor

specific

blo

ckin

g p

robabili

ty, B

i

B2 fo r O

2= 100 E rl

B1 fo r O

2= 100 E rl

B2 fo r O

2= 6 0 E rl

B1 fo r O

2= 6 0 E rl

F ig . 6 . C oex istence of operators with sig nifi cantly different traffi c load andmax imu m capacity lev el Ci. H ere, C1 = 2 0 and C2 = 6 0 channels. T heoffered load for operator 1 is v aried and depicted for two different lev els ofoffered load for operator 2 (O2 = 6 0 and 1 0 0 E rlang ) .

detail. I t was shown that this method mak es it possib le to

differentiate the b lock ing prob ab ility of operators sharing the

same carrier(s) , in particu lar for serv ices with modest data

rates (lik e v oice telephony ).

F or fu rther work it wou ld b e interesting to analy z e the

performance with pack et switched connections, and the im-

plications of av erag ing load measu rements.

AC K N O W L E D GM E N T

T he au thors wou ld lik e to thank P rof. J ens Z ander (Roy al In-

stitu te of T echnolog y , S weden) and P reb en M og ensen (N ok ia

N etwork s, D enmark ) for their v alu ab le comments and feed-

b ack on this work .

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C ompetition, Reg u lation and Alternativ e T echnolog ies” , in P roceedings

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tu re” , In P roceedings of 3 G M ob ile Communication T echnologies, M ay8-1 0 , 2 0 0 2 .

[ 6 ] 3 rd Generation P artnership P roject ( 3 GP P ) , T echnical S pecifi cationGrou p (T S G) RAN 3 , “ S hared N etwork S upport in Connected M ode

(R elease 5 ) ” , T echnical report R3 .0 1 2 , v ersion 1 .0 .0 , S eptemb er 2 0 0 2 .[ 7 ] 3 rd Generation P artnership P roject ( 3 GP P ) , T echnical S pecifi cation

Grou p S erv ices and S y stem Aspects, “ S erv ice A spects and R eq uirements

for N etwork S haring (R elease 6 ) ” , T echnical report 2 2 .9 5 1 , v ersion6 .0 .0 , D ecemb er 2 0 0 2 .

Chapter 10

An Estimation of theAchievable UserThroughput with NationalRoaming (Paper 6)

Johan Hultell and Klas Johansson,Submitted to IEEE VTC2006 Spring.

117

119

An E s tim a tio n o f th e Ac h ie va b le U s e r

T h r o u g h p u t w ith N a tio na l R o a m ing

Johan H u lte ll, and K las Johans s on

Wireless@ K TH , Th e R oyal In stitu te of Tec h n ology

E lec tr u m 4 1 8 , S -1 6 4 4 0 K ista, S wed en

E m ail: {joh an .h u ltell, klasj}@ rad io.kth .se

Abstract— N a tion a l roa m in g , t h a t is a llowin g u s ers to a c c es sn etwork s of m u lt ip le d om es t ic op era tor’s , wa s d is c u s s ed a lrea d yd u rin g d evelop m en t of fi rs t a n d s ec on d g en era t ion m obile s y s -tem s . For m obile voic e s ervic es , h owever, t h e op era tors a fford edto bu ild n etwork s with ( a lm os t ) fu ll c overa g e a lon e. T h e ben efi t swith n a t ion a l roa m in g , in term s of in c rea s ed c overa g e, tru n k in geffi c ien c y a n d lowered ris k ex p os u re, d id c on s eq u en t ly n ot ex c eedt h e d ra wba c k s a s s oc ia ted with in c rea s ed op era tor c oop era t ion .

M otiva ted by t h e c on s id era ble in ves t m en t s for p rovid in g c ov-era g e for m obile d a t a s ervic es , a n d en a bled by t h e c om m onra d io res ou rc e m a n a g em en t p rop os ed for s y s tem s “ B ey on d 3 G ” ,t h is p a p er1 eva lu a tes t h e ben efi t s with n a t ion a l roa m in g for bes teffort d a t a s ervic es . T h e res u lt s s h ow th a t n a t ion a l roa m in g m oreth a n d ou bles t h e d a t a ra tes for u s ers a t t h e c ell bord er. T h isg a in is m otiva ted by red u c ed p a t h los s a n d lowered in terferen c elevels . A rou n d h a lf of th e p oten t ia l g a in is c a p t u red a lrea d y withn ea rly c o-s ited ba s e s t a t ion s , wh ic h s u g g es t s t h a t t h e ben efi t s forop era tors to c oord in a te t h eir ba s e s t a t ion s ite p la n s a re lim ited .

I. INTRODUCTION

D esp ite th e in trod u c tion of ad van c ed tran sm ission an d

p ac ket sc h ed u lin g tec h n iq u es, ex istin g m obile d ata n etworks

wou ld req u ire sign ifi c an t, an d h en c e c ostly, u p grad es in or-

d er to su p p ort h igh er d ata rates with wid e area c overage.

C on seq u en tly, th e vast m ajority of op erators h ave c h osen

to p ostp on e th e n ec essary n etwork u p grad es u n til c on su m er

d em an d bec om es m ore p ron ou n c ed . Alth ou gh th is at a fi rst

glan c e m igh t seem sou n d , it is d efi n itely n ot th e c ase sin c e

su p p ortin g h igh -en d u sers ( “ early ad op ters” ) h as p roven to be

a c r u c ial en abler for later reac h in g a m ass-m arket [ 1 ] .

O n e m eth od to in c rease c overage for h igh er d ata rates wou ld

be to en able u sers to roam between m u ltip le op erators with in

a c ou n try (h en c eforth referred to as national roam ing ) u n til

d em an d for h igh -sp eed servic es ju stifi es a n etwork ex p an sion .

Th e m eth od was d isc u ssed alread y d u r in g d evelop m en t of

th e fi rst an d sec on d gen eration system s, bu t for m obile voic e

servic es op erators afford ed to bu ild fu ll c overage n etworks

alon e. S u p p ortin g broad ban d d ata servic es are h owever asso-

c iated with m u c h larger in vestm en ts an d , as alon g as d em an d

is resilien t, r isks. M oreover th ere will, c om p ared to voic e

servic es, be fewer sim u ltan eou s u sers an d th u s larger sc op e for

statistic al m u ltip lex in g. All of th is yet again m akes n ation al

roam in g an in terestin g altern ative.

1Th is wo rk h a s b e e n c o n d u c te d with in th e No ve l A c c e s s P r o vis io n in g(NA P ) p r o je c t, c o - f u n d e d b y th e S we d is h A g e n c y fo r In n o va tio n S ys te m s(VINNOVA ).

With th is said , th e c h ief ad van tage with n ation al roam in g

is th at on ly sm all in vestm en ts are n eed ed wh ereas th e m ain

d rawbac k is th at it m igh t h ar m c om p etition between th e op -

erators an d th at th e in volved n etworks n eed to be c om p atible.

H en c e, it is of p aram ou n t im p ortan c e th at both th e ex c h an ge

of bu sin ess sen sitive in form ation (e.g. load m easu rem en ts) is

kep t at m in im u m level, wh ic h we d isc u ssed in [ 2 ] , an d th at

th e en ablin g fu n c tion ality rem ain sim p le so th at ex it barr iers

assoc iated with n etwork sh arin g agreem en ts are sm all [ 4 ] .

In p rac tise th is m ean s th at ex istin g fu n c tion ality sh ou ld be

reu sed as far as p ossible wh ereas n ew m ec h an ism s ou gh t to

be d esign ed in a m od u lar fash ion [ 3 ] .

A sim ilar c on c ep t is geograp h ic al sh arin g of wireless in fras-

tr u c tu re. It h as p reviou sly been p rop osed as a m eth od to lower

in frastr u c tu re c osts an d savin gs in th e ord er of 1 0 p er c en t of

a m obile op erator’s total c ost h as been rep orted [ 4 ]-[ 5 ] . Th is

is, h owever, p r im ar ily a m eth od for lowerin g roll-ou t c osts

for n ew rad io ac c ess tec h n ologies wh ereas n ation al roam in g

rath er is abou t d eferr in g in vestm en ts by sh arin g ex istin g, an d

th u s alread y d ep loyed , in frastr u c tu re. Th e p roblem of allo-

c atin g u sers to th e ap p rop r iate base station in h eterogen eou s

m u lti-n etwork en viron m en ts h as attrac ted sign ifi c an t atten tion

in th e literatu re [ 6 ]-[ 1 2 ] . D yn am ic resou r c e m an agem en t of

m u ltip le wireless n etworks, ac ross rad io ac c ess tec h n ologies

an d bu sin ess bou n d aries, is also a m ain th em e in th e in tegrated

E U p rojec t Am bien t N etworks [1 0 ] . A th orou gh in vestigation

of h ow u sers sh ou ld be alloc ated with in on e op erator’s m u lti-

ac c ess n etwork was c on d u c ted in [ 8 ] an d it was sh own th at

sign ifi c an t gain s c ou ld be ac h ieved in a c om bin ed G S M /E D G E

an d WC D M A sc en ario. Also [9 ] ex am in ed gain s th at c ou ld be

attain ed by ex p loitin g th e c oex isten c e of m u ltip le system s to

in c rease th e tr u n kin g effi c ien c y. It was c on c lu d ed th at c om m on

rad io resou r c e m an agem en t with fou r in tegrated n etworks

d ou bles th e “ in terac tive p ac ket c ap ac ity” . O th er stu d ies, su c h

as [1 1 ]-[ 1 2 ] h ave id en tifi ed c om p lem en tary m etr ic s su c h as

m on etary c ost, u ser p referen c es, etc ., th at c ou ld be c on sid ered

wh en alloc atin g u sers in m u lti-op erator en viron m en ts.

In th is p ap er we will in vestigate wh at th rou gh p u t op erators

c an ex p ec t with n ation al roam in g for u sers loc ated at th e c ell

bord er as well as in average. M oreover we stu d y if, an d to wh at

ex ten t, th ese gain s d ep en d on th e m ic rosc op ic d iversity ord er

an d th e relative site loc ation s of th e in volved system s ( d egree

of overlap ). Th e stu d y is lim ited to u p lin k tran sm ission , sin c e

th at typ ic ally lim its c overage in c ellu lar system s.

120 Chapter 10. Throughput with National Roaming (Paper 6)

II. SYSTEM MODEL

B es id es in trod u cin g a m od el for q u an tifyin g th e d egree of

overlap between in volved s ys tem s , th is s ection p res en ts th e

u s ed p rop agation m od el an d m u lti-acces s s ch em es .

A. U s e r a n d Tra ffi c B e h a v io r

A m u lti-op erator en viron m en t with J op erators is in ves -

tigated . T h rou gh ou t th e p ap er all active u s ers are s tation ary

an d u n iform ly d is tr ibu ted . T h is res u lts in th at th e traffi c load

for op erator j is P ois s on d is tr ibu ted with ex p ected valu e ωj

( m eas u red in u s er s p er k m 2) . Moreover we, for s im p licity

reas on s , as s u m e th at all u s er s h ave fu ll bu ffers an d d em an d

bes t effort traffi c.

B . M u lti- O p e ra to r E n v iro n m e n t a n d B a s e S ta tio n D e p lo y m e n t

N ation al roam in g en ables u s er s to con n ect to an y of th e Jcoop eratin g op erators ’ bas e s tation s . For th e s ak e of s im p licity

all op erators are as s u m ed to d ep loy bas e s tation s , eq u ip p ed

with m u ltip le th ree-s ector an ten n as , in a h ex agon al grid (with

corres p on d in g bas e s tation d en s ity ρj ) . E ach op erator h as on e

carr ier freq u en cy, wh ich is u s ed in every cell ( reu s e factor 1).

T h e coverage an d cap acity gain s attain ed from n ation al

roam in g d ep en d on th e em p loyed h an d off algorith m , an d th e

relative p os ition of th e s ites belon gin g to th e op erators . T h e

latter is cap tu red by th e in te r- o p e ra to r s ite d is ta n c e d, wh ich

we d efi n e as th e m in im u m d is tan ce between ad jacen t s ites

belon gin g to d ifferen t op erators . T h u s for a s cen ario with Jop erators

d = m in(d1, d2, ..., d(J

2)

). (1)

Alth ou gh d1 6= d2 6= ... 6= d(J

2)in gen eral it can be s h own 2

th at d ep loym en ts wh ere d1 = d2 = ... = d(j

2)ex is t if J belon g

to th e s o-called m agic n u m bers . For a th ree-op erator s cen ario,

th e relation s h ip of th e op erators ’ s ite location s are given as

(xi

yi

)=

(x0

y0

)+ d

(co s (π/6 )

(−1)is in (π/6 )

), ( 2 )

wh ere i = 1, 2 an d (x0, y0)T

is th e p os ition of th e referen ce

n etwork . Figu re 1 d ep icts an ex am p le wh ere d = rc an d in

Section IV th e in ter-op erator s ite d is tan ce will be u s ed to

con trol th e d egree of overlap between th e n etwork s .

C . P ro p a g a tio n M o d e l

G iven a u s er p os ition , th e s lowly varyin g (ex p ected ) com -

p on en t of th e p ath gain between u s er k an d bas e s tation l can

be written in logarith m ic s cale as

µkl = Gkl + Gs [ d B ]. ( 3 )

Gkl d en otes th e d eterm in is tic p art of th e p ath gain an d is

h erein d es cribed by th e C O ST 2 3 1-H ata m od el [13 ] . U s in g

s tan d ard p aram eters for an ou td oor u rban s ettin g (i.e. bas e

2I t c an b e s h o w n th at th is is p o s s ib le if th e n u m b er o f n etw o r k s J b elo n gto th e s o - c alled m ag ic n u m b er s {1 ,3 ,4 ,7 ,9 ,...}.

1500 1000 500 0 500 1000 1500 2000

1500

1000

500

0

500

1000

1500

1

2

3

4

7

8

9

10

11

12 13

14

15

16

17

18

19 20

21

1

2

3

4

7

8

9

10

11

12 13

14

15

16

17

18

19 20

211

2

3

4

7

8

9

10

11

12 13

14

15

16

17

18

19 20

21

7

d1

rc

2

3

d

d

F ig . 1 . A n ex am p le o f th e b as e s tatio n d ep lo y m en t f o r a th ree-o p erato rs c en ario an d in ter- o p erato r s ite d is tan c e d = rc= 3 6 0 m . I t s h o u ld b e n o ted

th at th e m ax im u m valu e o f d is p er io d ic w ith a p erio d 2√

3rc

s tation h eigh t of 3 0 m , u s er h eigh t of 1.5 m an d a 3 d B correction

factor) it red u ces to

Gkl = − (28.9 + 3 3 .9log10

fc + 3 5 .2log10

rkl) [ d B ], ( 4 )

wh ere fc is th e carr ier freq u en cy in MH z an d rkl is th e

d is tan ce between th e bas e s tation an d th e m obile term in al.

T h e s econ d p art of E q . ( 3 ) corres p on d s to th e s p atially

correlated log-n orm al s h ad ow fad in g (s low fad in g). With in th is

s tu d y it is as s u m ed to h ave a s tan d ard d eviation σs = 8d B

an d a d ecorrelation d is tan ce of 2 0 m [14 ] - [15 ] . B es id es th e

s low fad in g d es cribed above, th e an ten n a elem en ts on th e

bas e s tation s are as s u m ed to be ex p os ed to in d ep en d en t fl at

R ayleigh fad in g with ex p ected valu e eq u alin g µkl. T h u s th e

p ath gain between a u s er an d its corres p on d in g bas e s tation

can be d es cribed by M id en tically, in d ep en d en tly R ayleigh

d is tr ibu ted ran d om variables wh ere M rep res en ts th e n u m ber

of an ten n a elem en ts .

D . M u lti-Ac c e s s S c h e m e

We s tu d y two s ch ed u lin g m eth od s for s h arin g th e d ata

ch an n el an d d u e to s im p licity reas on s we as s u m e th at u s er s

on ly can con n ect to on e bas e s tation at a tim e u s in g T D MA.

H en ce we d o n ot accou n t for p oten tial d ivers ity gain s from s oft

h an d over (or rath er, in th e cas e of T D MA, fas t cell s election ) .

T h e fi r s t m u lti-acces s s ch em e con s is ts of a rou n d robin ( R R )

s ch ed u ler wh ere u s ers in a p articu lar cell s h are th e ch an n el in a

ran d om fas h ion . O n average th ou gh , all u s er s in th e cell obtain

th e ch an n el an eq u al am ou n t of th e tim e ( “ tim e- h op p in g” ) .

C om p ared to u s in g a cyclicly rep eated s ch ed u le th is yield s

s m ooth er in terferen ce s tatis tics .

In th e s econ d m eth od a p rop ortion fair ( P F) s ch ed u ler is

u s ed . C on trary to th e R R s ch ed u ler, wh ich ign ore th e ch an n el

con d ition s , th e P F s ch ed u ler ex p loit th e fas t fad in g variation s

an d as s ign s th e ch an n el to th e u s er ex p erien cin g th e bes t

relative ch an n el q u ality. For bas e s tation l, th e ch an n el q u ality

as s ociated with u s er k is m eas u red as

121

Qkl =

∑M

m=1Gklm

µkl

[lin], ( 5 )

w h ere Glkm c o r res p o nd s to ind ep end ent ex p o nentially d is -

tr ib u ted variab les . Th e nu m erato r es tim ates th e ins tantaneo u s

c h annel q u ality and th e d eno m inato r d es c r ib es th e ex p ec ted

c h annel q u ality . B as ed o n th is m eas u re, eac h b as e s tatio n

s c h ed u le th e u s er fu lfi lling

k = argm ax Qlk

k∈kl( 6 )

w h ere kl is th e s et o f u s er s c o nnec ted to b as e s tatio n l.

Fo r b o th s c h ed u ling s trategies th e average trans m it p o w er is

lim ited to th e m ax im u m o f an average valu e o f P = 2 4 d B m and

a p eak valu e o f Pm a x = 3 3 d B m . C o ns eq u ently a u s er c o nnec ted

to a b as e s tatio n w ith N − 1 o th er s im u ltaneo u s u s er s u tiliz e

a trans m it p o w er given b y

Pk = m in(PN, Pm a x

)[ lin]. ( 7 )

E. D a ta R a te Es tim a tio n

As s u m ing th at u s er k b elo ngs to th e s et o f u s er s th at are

s c h ed u led to trans m it d u r ing tim e-s lo t kt, th e rec eived s ignal-

to - interferenc e (SIR ) ratio at antenna elem ent m b elo nging to

b as e s tatio n l c an b e w ritten as

γklm =PkGklm∑

i=kt\kPiGim + N0W

, ( 8 )

w h ere N0W is th e rec eived no is e p o w er ( m o d elled as AWG N

w ith c o ns tant s p ec tral d ens ity ) . As s u m ing s y nc h r o no u s m ax i-

m u m ratio c o m b ining (MR C ) , th e res u lting ins tantaneo u s SIR

is o b tained as

Γkl =

M∑

m=1

γklm. ( 9 )

U s ing th es e SIR valu es , th e d ata rates are es tim ated th r o u gh

th e Sh anno n m o d el w ith a m ax im u m p eak b it rate Rm a x

ac c o r d ing to

Rkl = m in(Rm a x ,W lo g2(1 + Γkl)), ( 1 0 )

w h ere Rma x is th e m ax im u m p eak th r o u gh p u t th at th e m o d -

u latio n and c o d ing s c h em es s u p p o r t. Th is is s et to eq u al

th e 9 0 th p er c entile o f p eak th r o u gh p u t ( p er tim e s lo t) in th e

s ingle o p erato r c as e w ith fo u r antennas . In th is c as e Rma x =2 0 Mb p s (es tim ated th r o u gh s im u latio ns ) . D u e to o u r id eal

as s u m p tio ns and m o d elling th is is h igh er th an in p rac tic al

s y s tem s .

F. M in im u m Pa th - L o s s B a s e S ta tio n S e le c tio n

We as s u m e th at u s er s c o nnec t to th e b as e s tatio n as s o c iated

w ith th e h igh es t lo ng-term average p ath gain µkl. C o ns e-

q u ently u s er k c o nnec ts to b as e s tatio n l w h ere

l = argm ax µkl

l∈l ( 1 1 )

and l is th e s et o f b as e s tatio n th at th e u s er c an s elec t.

TAB LE I

SYSTEM PARAMETERS

Pa ra m e te r Va lu e

Po p u la tio n d e n s ity [ k m −2] 50 0 0# Op e r a to r s 3

Slo t d u r a tio n [ m s ] 2Co rr e la tio n d is ta n c e [ m ] 20

Sta n d a r d d e via tio n ( s h a d o w fa d in g ) [ d B ] 8Sh a d o w fa d in g c o r r e la tio n b e twe e n b a s e s ta tio n s 0 .5

Pa th lo s s @ 1 m [ d B ] -35.8Dis ta n c e d e p e n d e n t a tte n u a tio n fa c to r 3.5

Ce ll r a d iu s [ m ] 36 0Ch a n n e l b a n d wid th [MH z ] 5Th e r m a l n o is e fl o o r [ d B m ] - 1 0 3

# Re c e ivin g a n te n n a s 2,4Ave ra g e te r m in a l p o we r [ d B m ] 24

Ma x im u m te r m in a l p o we r [ d B m ] 33# Se c to r s p e r b a s e s ta tio n 3

Tra ffi c [ u s e r s /k m 2] 1 ,3,1 0In te r s ite d is ta n c e [ m ] 0 - 6 23.5

Sinc e th e p ath lo s s is rec ip r o c al th e algo rith m d es c r ib ed

b y E q . ( 1 1 ) c an b e im p lem ented in a d is tr ib u ted fas h io n

w h ere th e term inals , s u b s eq u ent to c o nd u c ting th e nec es s ary

m eas u rem ents s elec t th e ap p r o p r iate b as e s tatio n. N o tic e th at

th is fac ilitates a s o - c alled m o b ile c o ntr o lled h and o ff, w h ic h

p revio u s ly h as b een id entifi ed as a d es irab le p r o p er ty in m u lti-

o p erato r enviro nm ents s inc e it am o ngs t o th er red u c es th e

s ignaling b etw een th e invo lved o p erato r s [ 7 ] [ 1 2 ] . As w e

neglec t effec ts o f m eas u rem ent err o r s , w e im p lic ity as s u m e

th at th e term inals h ave th e ab ility to m eas u re th e p ath gain

id eally fo r th e entire c and id ate s et o f b as e s tatio ns .

G enerally h and o ver algo rith m s als o inc o r p o rating lo ad in-

fo r m atio n o u tp erfo r m th o s e th at m erely b as e th eir d ec is io n o n

rec eived s ignal q u ality [ 6 ] . Th is is h o w ever d u e to lim ited

s p ac e o u ts id e th e s c o p e o f th is p ap er.

III. PERFORMANCE MEASURES AND SIMULATION MODEL

Th is s ec tio n b riefl y d es c r ib es th e s im u latio n enviro nm ent

u s ed to q u antify ing th e gains in u s er th r o u gh p u t as s o c iated

w ith natio nal r o am ing.

A . Pe r fo r m a n c e M e a s u r e s

O ne ru d im entary m eas u re o f th e b enefi t w ith natio nal r o am -

ing is th e ac h ievab le u s er th r o u gh p u t. In th e p ap er w e u s e th e

average u s er th r o u gh p u t, w h ic h als o c o r res p o nd s th e s y s tem

th r o u gh p u t, to m eas u re gain an u s er ac h ieves in average. As

no m inim u m gu aranteed d ata rates h ave b een as s u m ed (and

c o ns eq u ently , no ad m is s io n c o ntr o l h ave u s ed u s ed ) w e u s e

th e average d ata th r o u gh p u t fo r th e 1 0 th u s er p erc entile, w h ic h

d es c r ib es th e s itu atio n fo r u s er s lo c ated at th e c ell b o r d er.

B . S im u la tio n M o d e l

Th ro u gh o u t th e s im u latio ns w e as s u m e th at th ree o p erato r s

c o ex is ts . Po tential b o r d er effec ts are m itigated th r o u gh an

im p lem ented w rap -aro u nd tec h niq u e and w ith in o u r s im u la-

tio ns th e inter- o p erato r s ite d is tanc e d, lo ad ω and nu m b er

o f rec eiving antennas M are varied . R em aining s im u latio ns

p aram eters are s u m m ariz ed in Tab le I.

122 Chapter 10. Throughput with National Roaming (Paper 6)

Inter−operator site distance 78m Inter−operator site distance 6 2 4 m0

5 0

1 00

1 5 0

2 00Two receiving antennas and round robin scheduling


2 0

4 0

6 0

80

1 00

1 2 0Four receiving antennas and proportional fair scheduling

Gai

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(a) G ain in 1 0 - p er c en tile u s er th rou gh p u t


1 0

2 0

3 0

4 0

5 0Two receiving antennas and round robin scheduling


5

1 0

1 5

2 0

2 5

3 0

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Four receiving antennas and proportional fair scheduling



(b ) G ain in average u s er th rou gh p u t

F ig. 2 . Th e relative gain in u s er th rou gh p u t for th e 1 0 th p er c en tile an d average u s er th rou gh p u t w ith n ation al roam in g. Th ree load s ωj ∈ {1, 3, 10 } arep res en ted for tw o variou s s y s tem s .

IV. NUMERICAL RESULTS

T h is s ec tion s tar ts with an gen eral evalu ation of th e th rou gh -

p u t gain s a m ob ile op erator c an ex p ec t with n ation al roam in g.

T h en , we an alyz e h ow th es e gain s d ep en d on th e m ic ros c op ic

d ivers ity ord er an d th e in ter-op erator s ite d is tan c e of th e in -

volved s ys tem s . Notic e th at eac h op erator op erator is res tr ic ted

to th eir own c arr ier als o in th e c as e of n ation al roam in g. A

join t f req u en c y p lan wou ld in c reas e th e gain as s oc iated with

n ation al roam in g even f u r th er.

A. G a in s w ith N a tio n a l R o a m in g

T h e u p p er p art of F igu re 2 p res en ts th e relative gain of th e

average d ata th rou gh p u t for th e 1 0 th u s er p erc en tile as well

as for th e average u s er in a s ys tem u tiliz in g two rec eivin g

an ten n as an d a RR s c h ed u ler. T h ree load s ωj ∈ {1, 3, 10 } and

two inter-op erator s ite dis tanc es are c ons idered.

T h e relative gain for th e 1 0 th u s er p erc entile varies between

7 9 – 1 8 5 % ( 1 0 0 % c orres p onds to a dou bling) and alth ou gh th e

gain for th e average u s er s m aller it is s till s ignifi c ant. T h e

gains are m ainly m otivated by redu c ed p ath -los s , bu t als o

aris e from lowered average interferenc e levels . From th e fi gu re

we fu r th er m ore s ee th at th e gains inc reas e with both inter-

op erator s ite dis tanc e and load. T h e latter c an, at leas t p artially,

be ex p lained by th at interferenc e levels at low loads already

with ou t national roam ing is s m all. T h is m ak es th e interferenc e

redu c tion enabled by national roam ing les s p rom inent. For th e

1 0 th u s er p erc entile th e differenc e is ac c entu ated as th os e u s ers

typ ic ally are loc ated in c ells with h igh loads . A t low loads ,

th is is c om p ens ated by inc reas ed trans m it p ower (s ee E q . 7 ) ,

wh ic h redu c es th e relative interferenc e level even fu r th er.

B. E ffe c t o f H ig h e r M ic r o s c o p ic D iv e r s ity

T h e lower p art of Figu re 2 p res ents th e c orres p onding

gains for a s ys tem u tiliz ing fou r rec eiving antennas and a P F

s c h edu ler. C om p ared to th e th os e rep orted above, we s ee th at

th e relative gains as s oc iated with national roam ing are redu c ed

for m ore advanc ed s ys tem s . N otic e h owever th at c ons iderable

gains s till ex is ts . T h e gain redu c tion aris es s inc e s ys tem s em -

p loying op p ortu nis tic s c h edu ling, m ore rec eiving antennas or

in any tec h niq u e im p roving th at ex p loits m ic ros c op ic divers ity

in order to im p rove th e link q u ality are as s oc iated with h igh er

S I R and, th u s , als o u s er th rou gh p u t levels . H enc e th e relative

gain m ay dec reas e even th ou gh th e abs olu te one inc reas es .

T h is fac t is dep ic ted in Figu re 3, wh ic h p res ents th e data rate

for th e 1 0 th u s er p erc entile. B es ides th e two afore des c r ibed

s ys tem s , one u s ing fou r antennas and R R s c h edu ling and one

u s ing two antennas and a P F s c h edu ler are treated.

From Figu re 3 it is c lear th at u s er th rou gh p u t inc reas es wh en

a P F s c h edu ler is u s ed ins tead of a R R s c h edu ler. A dditionally,

it c an be s een th at th e relative gain as s oc iated with th e P F

s c h edu ler inc reas es as th e load, and h enc e m u ltiu s er divers ity,

inc reas es . In s ys tem s with h igh loads , e.g. ωj = 10 u s er s p er

c ell, it yields gains s im ilar to th os e obtained with national

roam ing. N otic e h owever th at a P F s c h edu ler gives s ignifi c ant

gains als o for s m all loads . T h is , at fi r s t rath er u nex p ec ted

res u lt, ar is es s inc e th e nu m ber of u s ers in a c ertain c ell is

P ois s on dis tr ibu ted. T h u s th e “ wors t” u s er s are, even at low

loads , lik ely to be in c ells wh ere th e wireles s c h annel is

s h ared with s everal oth ers s o th at a m u ltiu s er divers ity is

p res ent. Finally, we note th at adding m ore rec eiving antennas

ou tp erform s P F s c h edu ling at low loads wh ereas th e gains

c oinc ides at h igh er loads .

C . E ffe c t o f I n c r e a s e d I n te r - O p e r a to r S ite D is ta n c e

I r res p ec tively of s c h edu ling s trategy, nu m ber of rec eiving

antennas and s ys tem load, th e u s er th rou gh p u t for th e 1 0 th

u s er p erc entile initially inc reas es rap idly with d. H owever,

at dis tanc es c om p arable to th e dec orrelation dis tanc e u s er

123

0 100 200 300 400 500 6000

1

2

3

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6

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[Mbp

s]

In te r−o p e ra to r s ite d is ta n c e [m ]

1 u s e r/c e ll

3 u s e rs /c e ll

10 u s e rs /c e ll

T w o a n te n n a s − R RT w o a n te n n a s − P FF o u r a n te n n a s − R RF o u r a n te n n a s − P F

Fig. 3 . Th e 1 0 - p er c en tile o f u ser th r o u gh p u t as a fu n c tio n o f in ter- o p erato rsite d istan c e fo r d ifferen t an ten n as an d p ac ket sc h ed u lin g strategies. No te th atc o -sitin g (0 m site d istan c e) c o r resp o n d to th e sin gle-o p erato r c ase.

throughput s aturates . T his s ugges ts that, for bes t effort traffi c

an d a m in im um path los s han d off algorithm , on ly m in or

c overage gain s are obtain ed by “ optim al” c o-plan n in g of the

n etwork s . Yet, it is of c ours e of prin c ipal im portan c e that

the s ites are n ot c o-loc ated , in whic h c as e n o m ac ros c opic

d ivers ity gain s are ac hieved .

S im ilarly as for the 1 0 th us er perc en tile the average us er

throughput in c reas es rapid ly with in c reas in g in ter-operator s ite

d is tan c e d. A roun d half of the gain is obtain ed alread y when

s ites are s eparated with 7 8 m , jus t above the d ec orrelation

d is tan c e of the s had ow fad in g. H en c e, a join t s ite plan n in g

is n either m otivated from a s y s tem c apac ity pers pec tive. D ue

to lim ited s pac e thes e res ults are n ot in c lud ed in the paper.

D. Va lid ity o f R e s u lts

T he valid ity of our pres en ted res ults are n aturally s ub-

jec t to the applied s y s tem m od ellin g an d as s um ption s . For

in s tan c e, in the s im ulation all operators were as s um ed to

have the s am e ty pe of n etwork (bas e s tation d en s ity , rad io

ac c es s tec hn ology , etc .) . M oreover we as s um ed id eal path

los s m eas urem en ts an d bes t effort traffi c with full buffers ,

whic h s tron gly effec ts , for ex am ple, the m ultius er d ivers ity

gain . A ll of the aforem en tion ed as s um ption s are lik ely to

y ield optim is tic perform an c e gain s when m eas ured in abs olute

n um bers . H owever, if the s am e m od ellin g errors are m ad e in

the s in gle operator referen c e c as e, the relative c om paris on s

s hould n ot be s ign ifi c an tly affec ted . H en c e, even though the

us er throughput of real n etwork s are un lik ely to be the s am e, it

is reas on able to believe that the relative gain s as s oc iated with

n ation al roam in g would be s im ilar as thos e pres en ted here.

V. CONCLUSIONS

I n this paper poten tial gain s as s oc iated with n a tio n a l ro a m -

in g , where operators s hare the wireles s in fras truc ture in ord er

to in c reas e the c overage for higher d ata rates , have been

s tud ied . T hroughout the s tud y ac tive us ers were as s um ed

s tation ary an d c on n ec ted to the bas e s tation with highes t

res ultin g path gain .

E n ablin g n ation al roam in g c an in this s c en ario d ouble the

d ata rates that us ers c los e to the c ell bord er ac hieves , even

with n early c o-loc ated bas e s tation s . T he gain is partly d ue to

red uc ed path los s , but als o from a lower average in terferen c e

level, whic h us ers c los e to the bas e s tation als o ben efi t of.

E ven though the relative gain in c reas e with in c reas in g in ter-

operator s ite d is tan c e an d traffi c load , alm os t half of the gain

aris e from m ac ros c opic d ivers ity again s t s had ow fad in g. T his

s ugges ts that the ben efi ts for operators to c o-plan their bas e

s tation s ites with n ation al roam in g are lim ited .

M oreover we c om pared the gain s with n ation al roam in g

to a few other (s tan d ard ) tec hn iq ues to in c reas e c apac ity an d

c overage, s uc h as m ulti-us er d ivers ity s c hed ulers an d rec eive

d ivers ity with m ultiple an ten n as . I t was s hown that the gain

with n ation al roam in g is s ign ifi c an tly higher, es pec ially for

low traffi c load , in whic h c as e the preferable ord er (without

c on s id erin g c os ts or other ec on om ic variables ) is n ation al

roam in g, m ore an ten n a elem en ts an d las tly m ore ad van c ed

s c hed ulin g s trategies .

N atural ex ten s ion s to this work would be to s tud y the effec ts

of m ore s ophis tic ated han d off algorithm s , the in fl uen c e of

in hom ogen eous us er d is tribution s , m eas urem en t errors , an d

ad d ition al c apac ity gain s obtain able in s y s tem s with ad m is s ion

c on trol ( d ue to trun k in g effi c ien c y ) .

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