self organising cloud cells

TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIESTrans. Emerging Tel. Tech. 2013; 00:1–13

DOI: 10.1002/ett

Self Organising Cloud cells: A Resource Efficient NetworkDensification StrategyTalal Alsedairy∗, Yinan Qi∗, Ali Imran †, Muhammad Ali Imran∗ and Barry Evans∗

∗Centre for Communication Systems Research University of Surrey, Guildford GU2 7XH, Surrey, U.K†School of Electrical and Computer Engineering, University of OklahomaEmail: [email protected]

ABSTRACT

Network densification is envisioned as the key enabler for 2020 vision that requires cellular systems to grow in capacityby hundreds of times to cope with unprecedented traffic growth trends being witnessed since advent of broadband on themove. However, increased energy consumption and complex mobility management associated with network densificationsremain as the two main challenges to be addressed before further network densification can be exploited on a wide scale. Inthe wake of these challenges, this paper proposes and evaluates a novel dense network deployment strategy for increasingthe capacity of future cellular systems without sacrificing energy efficiency and compromising mobility performance. Ourdeployment architecture consists of smart small cells, called cloud nodes, that provide data coverage to individual userson a demand bases while taking into account the spatial and temporal dynamics of user mobility and traffic. The decisionto activate the cloud nodes such that certain performance objectives at system level are targeted, is done by the overlayingmacro cell based on a fuzzy-logic framework. We also compare the proposed architecture with conventional macro cellonly deployment as well as pure micro cell based dense deployment in terms of blocking probability, handover probabilityand energy efficiency and discuss and quantify the tradeoffs therein.Copyright c© 2013 John Wiley & Sons, Ltd.

1. INTRODUCTION

In recent years, there has been a tremendous increase inthe number of mobile handsets, in particular smartphones,supporting a wide range of applications, such as imageand video transfer, cloud services and cloud storage.Consequently, the average smartphone usage rate hasnearly tripled in 2011 alone and the overall amount ofmobile data traffic demand grew by 2.3 times [1] inthe same period. Furthermore, the amount of mobiledata traffic is expected to increase dramatically in thecoming years; recent forecasts are expecting the datatraffic to increase by more than 500 times in the next tenyears [2, 3]. If the current traffic demand growth rate ismaintained, current cellular system capacity will not beable to cope with it. Therefore future cellular systems haveto be designed to contain the expected traffic growth. Theincrease of traffic demand leads to the need for furtherdensification of the network, especially in areas wheretraffic demand is the highest (hotspot areas). Althoughfurther densification provides means to overcome thisincrease of traffic, it proposes several challenges that needto be addressed, such as increased signalling level due tothe increased handover events and dramatic increase in thepower consumption.

Moreover, traffic load varies from time to time suchas the typical night-day behaviour of users and theirdaily swarming to offices and back to residential areas[4]. While traffic varies the power consumption of theradio access network does not effectively scale with it. Inmobile networks, 10% of the overall power consumptioncorresponds to the cellular users whereas 90% is incurredby the operator network [5]. The mobile network accesspart consumes a huge amount of energy in the basestation operation. As network densification is envisionedas a key source to accommodate the gigantic capacitiesexpected from future cellular networks, the high energyconsumption and mobility related overheads are emergingas even bigger challenges.

As a back drop of these challenges, in this paperwe present a novel network densification strategy thatexploits the notion of demand based cloud cell coverage tominimize the energy consumption as well as the handoverrelated overheads, while maintaining QoS thresholds e.g.in terms of blocking probabilities. The proposed solutionhas the ability to self organize the network deploymentin order to gain the potential resource efficiency that canbe harnessed from the spatio temporal dynamics of usertraffic, that are inherent to any cellular system.

Copyright c© 2013 John Wiley & Sons, Ltd. 1Prepared using ettauth.cls [Version: 2012/06/19 v2.10]

The remainder of this paper is organised as follows,section II presents related work. Section III explainsour proposed alternative deployment strategy. Section IVdescribes the proposed dynamic network optimisationframework and section VI provides the simulation results.Finally, section VII concludes the work.

2. RELATED WORK

This section presents the state-of-the-art in the deploymentstrategies that aim to achieve an energy-efficient deploy-ment for the radio access parts of a cellular system. Wefocus mainly on two approaches, heterogeneous deploy-ment and a network management approach for their rele-vance to this work. The heterogeneous deployment aims tooffload traffic from the macrocell to small cells deployedin the area of the overlay network. On the other hand thenetwork management approach adopts a self organisingmethodology to manipulate the active node deployment byswitching on/off the nodes, with the aim of saving energy.

It is widely believed that using a mixed topology ofmacro with femto or microcells could lower the energyconsumption for a targeted achievable capacity, thereby,providing a heterogeneous deployment approach. It iswell known that radio signals are subject to variouschannel attenuations. One of the major losses is buildingpropagation loss: the indoor users suffer, compared tooutdoor users, because of in-building penetration loss. Thisimplies that the radio links that are subject to high lossesare the most expensive in terms of macrocell resources.Therefore, by deploying femtocells, macrocell resourcessuch as capacity and energy can be saved. Such offloadingbenefits of femtocells are discussed in [6]. Moreover,an advantage of deploying micro base stations is theirability to scale their power consumption to their activitylevel [7]. By exploiting such scaling, the deploymentof microcells with macrocells gives the advantage ofhaving a larger power saving compared to normal macrodeployment to achieve a targeted spectrum efficiency andhigher throughput [8, 9, 10].

On the other hand, in order to cope with growingnetwork density, the replacement of human-driven (half-manual) network management solutions with techniquesproviding self-organising networks (SON) is also beingconsidered [11, 12]. Such solutions for retaining resourceefficiency fall under the category of network managementbased solutions. A promising approach of reducingthe overall energy consumption of mobile networksin this catagory is to reduce the number of activenetwork elements. This approach involves dynamicallyswitching base stations OFF, thereby achieving a dynamicdeployment architecture. When a base station is switchedOFF, radio coverage and service quality must still beguaranteed (QoS) by neighbouring base stations or othermeans [13, 14, 15]. As the traffic load varies during aday, adaptively setting the bandwidth utilization according

to the this variation of traffic, thereby making the poweramplifier operate closer to its most efficient operationalpoint to achieve a more effective operation is an alternativeenergy saving scheme that does not require switching offon of nodes and thus avoids associated problems [16].Around 29% energy saving is expected when bandwidthadaptation is adopted and this can be much greater forareas with reduced load demand [16].

To summarize, to the best of our knowledge, mostof the heterogeneous deployment based approaches aswell as network management based approaches in theliterature generally consider the energy efficiency problemby optimising the system in its high or low demandregions and neglecting the other, both in time and space.These approaches also compromise on QoS in someaspects. On the other hand, our proposed deploymentframework provides a generic self-organizing solutionthat can optimise radio as well as energy wise resourceefficiency in future cellular systems both in high and lowdemand regions , without compromising on the quality ofservice criteria. In the next section we describe the coreidea behind our proposed framework.

3. CLOUD COVERAGE: ANALTERNATIVE TO CLASSIC STATICDENSIFICATION APPROACH

In 3GPP LTE Release 10, network densification bydeploying small cells, has been an important subjectto cover areas with high traffic growth. More recently,LTE Release 12 has also embarked on solutionscontaining small cell enhancements. To achieve an optimalperformance level and provide a cost- and energy-efficientoperation, small cells require further enhancements andmay be required to complement and have the ability tocommunicate with existing macrocells’ stations. However,network densification by the means of deploying smallcells proves to be a challenge, as small-cell deploymentcreates and increases inter- and intra-cell handover thatcan affect connectivity, especially in existing high-mobilityareas of the network [17]. Furthermore, network-widedeployment of small cells is difficult to operate andrequires careful cell planning [3]. Therefore, solutionsconsisting of small cell deployment must overcome thesechallenges.

For a given area to be covered, each cell size deploymentprovides a certain trade off. When comparing large-cell(i.e. macrocells) and small-cell (i.e. micro, pico, femtoand even WiFi nodes) deployment topologies, macrocellsoutperform smaller cells in terms of handover probability,which is expected since each smaller cell covers a fractionof the area, therefore, more handovers are expected andthus, increased signalling is expected. On the other hand,from the point of view of blocking probability, small cellsoutperform macro cell deployment which is one of thebenefits of small cell deployment [18]. In terms of power

2 Trans. Emerging Tel. Tech. 2013; 00:1–13 c© 2013 John Wiley & Sons, Ltd.DOI: 10.1002/ett

Prepared using ettauth.cls

consumption, as the area of coverage increases, small celldeployment power consumption increases and surpassesthe power consumption of macrocells for large coverageareas as the number of small cells increase to cover thearea [19].

As each deployment topology has its weaknesses andadvantages, considering which to deploy becomes a matterof perspective. For example, in a situation of low trafficdemand and sparse user distribution, the deployment ofa large cell is more efficient in terms of minimisinghandover and maximizing operational/energy efficiency.On the other hand, in a situation of high traffic demandand/or dense user distribution, the availability of smallcells is more beneficial due to the increase in theachievable capacity levels. Since traffic distributions anduser demographics are far from being fixed in space ortime, not even for duration of a day, none of the twodeployment solutions discussed above may be optimal.To this end, in this paper, we propose a deploymentstrategy that is a hybrid of both. However, to overcome thedrawback of traditional hybrid deployment i.e. increasedpower consumption and mobility management overheads,we propose to exploit the notion of cloud small cells tocompliment the macro cells as compared to conventionalsmall cells. Cloud small cells are smart small cells thatunderlay in the coverage area of the macro cell withhigh node density as shown in Fig. 1. However, insteadof being always on, these cells cooperate with theirparent macro cell to become available on demand i.e.the coverage provided by these small cells can effectivelyfollow the user and hence the name cloud cells and theterm cloud coverage. The operation of the cloud cellscan be controlled via the main macro station. The abilityto have cooperative mechanisms between macro cellsand small cells is already being envisioned in the 3GPPrelease 12. Thus, when user equipments (UEs) are in thecoverage of the macro cell, the macro BS can evaluatethe current situation in terms of traffic demand, currentsystem performance level, target system performancelevel, criticality of user, energy tariffs at the time of day andmany other similar factors to decide whether the activationof the respective cloud cells is needed or not. For example,if a certain part of the macro cell contains a large numberof UEs and the rest of the cell area has a low numberof users with low traffic demand requests, the macro cellcan offload the users in congested area by activating therespective cloud cells and handle the rest of the users onits own. This will consume less energy and will incur lesshandovers compared to a scenario where the total area iscovered by always on small cells. On the other side, itwill create more capacity compared to a scenario wherethe total area is covered only by macro cells.

Our proposed dynamic approach enables the networktopology to change based on current demand levelsand performance expectations. Once new or differentrequirements arise the proposed hybrid deploymenttopology can adapt with it. From the point of view of

scalability, since cloud-cell coverage does not have to becontinuous, initial deployment of cloud cells can be inthe most affected parts of the network, to gradually addcapacity. Further deployment can be based on capacityrequirements by increasing the number of cloud cellsin a given area. In the next section we present theoptimisation framework required to enable our cloud cellbased deployment architecture.

Figure 1. Cloud cell architecture.

4. DYNAMIC NETWORK OPTIMISATIONFRAMEWORK

Self-organization (SO) is not a new concept, it has beendefined in several fields, such as computer science andbiology [20]. The 3rd Generation Partnership Project(3GPP) aimed to provide a specification for agreeddescriptions of use cases and solutions with emphasison the interaction of self-optimization, self-configurationand self-healing. In [21] a main document describing theconcepts and requirements for self organizing networksis provided and the self establishment of eNodeBs in[22], self-optimization [23] self healing [24] and automaticneighbor management [25, 26]. The description in simpleterms of those details of SON as in Release 9 and Release10 can be found in [27, 28].

The self organising framework proposed in this paperis applicable for short to large term dynamics of cellularsystems [11], i.e. the density of active cloud cells canchange in a time scale of seconds to days in response to thecellular eco-system dynamics such as mobility, temporaryhotspots, or shadowing etc. The framework maximizesthe overall performance of the system while consideringthe needs of individual users in the macro base stationcoverage. The framework is based on self-organisationto exploit the benefits of small cell deployment whilstnot losing the benefits provided by macro station-baseddeployment, by dynamically adapting node density withuser associations. It aims to maximise performance levelsin terms of the desired key performance indicators (KPIs).As there are several performance indicators to measure

Trans. Emerging Tel. Tech. 2013; 00:1–13 c© 2013 John Wiley & Sons, Ltd. 3DOI: 10.1002/ettPrepared using ettauth.cls

a cellular system performance, the framework does notadopt a simple maximum or minimise the problem but ismodeled as a generic multiple-objective problem involvingseveral criteria such that other criteria can be included totailor the optimization objective according to the operator’sspecific policy requirements. However, in the scope of thispaper, we have focused our performance evaluation studyon the main KPIs namely achievable capacity, blockingprobability, handover probability and energy consumption.

Each base station (i.e. macro station) is responsiblefor forming a decision on which of the cloud nodesare active and if its services to users are required ornot. On the other hand, cloud nodes are responsible forserving the users in their small coverage area if they areactivated. Therefore, from the perspective of cloud nodesit is considered to be a centralised approach. On the otherhand, from the perspective of the network as a whole, itis a decentralised approach as the decision is carried outon a cell basis. The centralised approach benefits from anoverall picture of the cell status and thus the macro stationcan manage the performance level with the knowledge ofthe impact of activating each node would have. On theother hand, the decentralisation in terms of the networkbenefits, the network in terms of the simplicity it providesand its suitability for cellular deployments in a widescenario. The most attractive feature of the proposed cloudcell approach is its self-organisation capability, regardlessof the functional architecture framework (centralised ordecentralised).

Figure 2 illustrates the main self-organisation conceptsincluded in the proposed cloud coverage approach. TheObservation and analysis stages are used to detect if thecurrent deployment is insufficient and then automaticallytriger issues a request to find an alternative deploymentapproach. Key Performance Indicators (KPIs) are used tomonitor the current status of the network and to formappropriate decisions to be made. KPIs can be averageblocking probability, power consumption, etc. Based onthe KPIs, the algorithm is executed in order to make adecision for a new cloud node activation. Finally, theexecution stage is responsible for enforcing the newarchitecture to be deployed.

Figure 2. Self-organisation cloud coverage for future cellularsystems.

5. DYNAMIC CLOUD COVERAGEEXECUTION (DCE)

The Dynamic Cloud Executioner (DCE) is located ateach main base station (i.e. at each macrocell). The DCEobjectives are as follows: (i) collect the necessary metricsto determine the status of the KPIs, (ii) provide a decisionand enforce it.

5.1. Decision-making for Self-organising CloudCoverage

We consider a heterogeneous network comprising differentbase station types that offer different cell coverage. To saveenergy, some of the network elements are switched off andothers are switched on to compensate and ensure QoS andcoverage. To decide which network elements are activeand which are not is considered to be a multiple-objectivedecision-making problem involving different networkcriteria and requirements. The conventional MADMmethods lack the ability to make an efficient decisionwhen imprecision or ambiguity is introduced to the data.Therefore, the use of fuzzy logic provides the ability todeal with imprecise data and also to evaluate multiplecriteria simultaneously to provide a robust mathematicalframework and can thereby be used to model nonlinearfunctions with arbitrary complexity. As network criteriaand requirements is changing with time in which cancause the decision on which network element are activeto fluctuate between two decision causing extra powerconsumption, loss of service, increased signaling and inthe long term can lead to hardware failure. Whereas, fuzzylogic has the ability to deal with this fluctuations moreefficiently due to the presence of the fuzzifier especiallyin areas where a simple true/false statement is insufficient.We adopt the weighted fuzzy-logic approach for themultiple objective decision-making (MODM) where theKPIs are used to generate a decision on which cloud cellshould be activated. Building on this feature of fuzzy logic,in our decision-making framework we consider each userindividually and obtain a decision that benefits the usersbased on their geographic distribution and requirement.As fuzzy logic provides the ability to compare, studyand evaluate multiple objectives simultaneously to providea robust mathematical framework for decision-makingtherefore moving from a binary decision to a MODM,making it the most suitable choice.

The block diagram of the proposed weighted fuzzy-logic system is presented in Fig. 3, where the systemKPIs are first normalised based on the desired achievableperformance of each KPI, for example, if we would like themaximum acceptable blocking probability to be ”0.001”then the blocking probability KPI would be scaled from”0” to ”1” based on the threshold of ”0.001”. Then thefuzzifier would convert the crisp KPIs to fuzzy sets. Themembership functions would be set in a way to have largermembership values for the desired outcome.



Figure 3. Block diagram of weighted fuzzy logic for cloudcoverage

Assuming that n KPIs are to be evaluated, eachKPI is input to the fuzzifier generating a fuzzy setsC1, C2, ..., Cn. Importance values are assigned for eachKPI using an analytic hierarchy process (AHP). The finalweights w are derived using an eigenvector method. Theweightings are then applied to each KPI for decision-making. The summation of each fuzzy KPI multiplied withits’ correspondent weight provides a preference value Ffor the given architecture to be adopted. If we assume thatwe have N (n = 1, 2, ...N ) KPIs and M (m = 1, 2, ...M )possible decisions to adopt a network architecture to bemade, then:

F(m) =N∑

i=1

ωim xi

n (1)

such that:

F(1) = ω11 x1

1 + ω21 x2

1 + ... + ωN1 xN

1

F(2) = ω12 x1

2 + ω22 x2

2 + ... + ωN2 xN

2

......

...

F(M) = ω1M x1

M + ω2M x2

M + ... + ωNM xN

M

(2)where x is the output of the fuzzifier of each KPI andω is the corresponding weight from the outcome ofthe AHP (analytic hierarchy process). At this point ifthe membership functions were designed to give greatervalues for the desired outcome, the decision becomes thelargest value of the decision function given in equation3. As the algorithm operates in the short term, severalchallenges arise. The decision on which architecture to beadopted depends on several criteria (KPIs) therefore, weaim to minimise the effects of maximising a given KPIsnegative impact on the other KPIs that might occur, asmaximising throughput can cause an increase in handoveroccurrence.As cloud nodes are activated and deactivateddynamically they impose several types of handover that

should be considered, i.e. Intra and Inter cell handover forboth cloud nodes and the macro cell.

Therefore, if we have J number of small cells (cloudnodes) deployed in the area of a macro station where eachcell has one of two possibilities: for macro cells it’s eitheractive (i.e. has active transmission), denoted by ”1” or insleep mode, denoted by ”0”. On the other hand, the smallcells (cloud nodes) are active denoted by ”1” or in availablemode, denoted by ”0” since when the small cells are notrequired to provide service only awaiting activation bythe macro cell in its domain (i.e. in a mode consumingless energy since only signalling is required). Therefore,the solution space is 21+J , as we also consider the useof the overlay macrocell to serve the traffic, such that{IBS , I1, I2, ..., IJ} where I ∈ {0, 1}.

5.2. Metrics Collection and KPIs Estimation

The DCE collects the necessary metrics to determine thestatus of each KPI to be able to generate a decision. Therequired information can be retrieved via an uplink controlchannel in which users transmit their normal measurementreport messages. In this paper the three main KPIsconsidered are: probability of blocking: indicating users’satisfaction, handover probability: indicating connectivityand mobility; power consumption: indicating the level ofconsumed power per cell area.

5.2.1. Blocking ProbabilityThe DCE determines the blocking probability of the cell

through the channel quality indicator (CQI) of each singleuser which represents the SNIR:

γu =Prx(u, i)

∑

j 6=i

Prx(u, j) + n0

=Ptx(i)PLu,iSu,iFu,i

∑

j 6=i

(Ptx(j)PLu,jSu,jFu,j) + n0

(3)

where γu represents the SINR of the uth user, indexi denotes the serving cell with j representing theinterfering cells. P (i) represents the transmitted signalpower including transmitter and receiver antenna gains,Su,i and Fu,i denotes shadowing (large-scale fading) andfast frequency selective fading respectively. PL denotesthe inverse of the path loss. Lastly, n0 represents the totalthermal noise.

From the SINR the transmission bit rate can beobtained using the adaptive modulation and coding (AMC)scheme. Several approaches to performing the mapping areavailable throughout the literature. Goldsmith and Chua[29] provide an analytic formula for a target bit error rate(BER) and for a Rayleigh fading channel:

ξu = log2

(

1 +−1.5γu

ln(5BER)

)

(4)

where ξu denotes the spectral efficiency in bits/s/Hz of theuth user, BER<(1/5)exp(-1.5) ≈ 4.46% and γu < 30 dB.


Table I. Adaptive modulation and coding.

SINR threshold Modulation Coding Rate Spectral(dB) m (bits/s/Hz) r efficiency

(bits/s/Hz)< 0.9 - - 0≥ 0.9 2 (QPSK) 1/3 0.66≥ 2.1 2 (QPSK) 1/2 1≥ 3.8 2 (QPSK) 2/3 1.33≥ 7.7 4 (16QAM) 1/2 2≥ 9.8 4 (16QAM) 2/3 2.66≥ 12.6 4 (16QAM) 5/6 3.33≥ 15 6 (64QAM) 2/3 4≥ 18.2 6 (64QAM) 5/6 5

Moreover, Schoenen, Halfmann, and Walke [30]propose a table-based alternative which is in line with3GPP LTE systems given in TableI.

From the achievable spectral efficiency we are able tocalculate the throughput of a user as follows:

thu(t) =Bw ξ(t)

Ui(5)

where Bw denotes the cell bandwidth, ξ(t) denotes theestimated channel spectral efficiency and Ui is the totalnumber of users in the ith cell that the user terminal isconnected with.

To obtain each user blocking probability we adopt aMMPP/M/l/D-PS queue (a single-server processor sharingqueue, with MMPP arrival process, Markovian servicetime) traffic model. User service rate is exponentiallydistributed with a mean value of μu = 1/Γ, where Γ is themean value of the service time. The amount of informationtransferred in each data connection is exponentiallydistributed with mean value R. Therefore, the dataconnection service time is exponentially distributedwith mean value μd = thu/R, where thu is the userthroughput. The steady state probability is defined asw(u, d), where u and d are the number of users and dataconnections respectively with a maximum of U users thatcan be admitted and a maximum of D data connections.The blocking probability is the probability of having a newuser or a data connection unable to be admitted for service.The MMPP is characterised by the infinitesimal generatormatrix Q(U+1)(D+1)×(U+1)(D+1):

Q =

Qu − Λ ΛμdI Qu − Λ − μdI Λ

∙ ∙ ∙μdI Qu − μdI

(6)where I is the identity matrix,

Λ =

0μd

2μd

. . .Uμd

(7)

and

Qu =

−λu λu

μu −(λu + μu) λu

. . .. . .

. . .μu −(λu + μu) λu

−μu μu

(8)

The steady probability is defined as thestationary vector π = (π0, π1, π2, ..., πD+1), whereπd = (πd,0, πd,1, πd,2, ..., πd,U) and πd,u = w(u, d) andsatisfies the following:

πQ = 0, πe = 1 (9)

From the steady state probability we can calculate theblocking probability as follows [31]:

pb =

D∑

d=0

πd,U λu +U∑

u=0

λD,u uλd

U∑

u=0

D∑

d=0

πd,u (uλd + λu)

(10)

5.2.2. Handover ProbabilityAs cloud nodes are activated and deactivated dynami-

cally they impose several types of handover that should beconsidered:

• InterB−B handover: represents a user terminalhanding over from a base station → to aneighbouring base station.

• InterC−B handover: represents a user terminalhanding over from a cloud cell → to a neighbouringbase station.

• IntraB−C handover: represents a user terminalhanding over from the parent base station → to acloud cell with in its area.

• IntraC−B handover: represents a user terminalhanding over from a cloud cell → to the parent basestation.

• IntraC−C handover: represents a user terminalhanding over from a cloud cell → to another cloudcell.

We consider the scenario of a mobile terminal located atpoint X (show in Fig.4) handing off from an old BS to afuture BS. We assume that cells are in a hexagonal shape,where the borders of the base stations are defined by thethreshold value of the received signal strength (RSS) thatwould initiate the handover process. Initially, the mobileterminal would be served by the old BS and is movingwith a velocity of v, which is uniformly distributed in[vmin; vmax]. We assume that a mobile terminal can movein any direction with equal probability, hence the pdf of themobile terminal direction of motion θ is [32]:

fθ =1

2π− π < θ < π (11)



Figure 4. The assumed handover scenario of a mobile terminal[32].

We also assume that the speed and direction of motionof a mobile terminal from point X until it goes out ofcoverage remains constant, since the distance from point Xto the cell boundary is assumed to be small given a densenetwork. At this point the mobile terminal would handoverwhen the direction of motion is between θ ∈ (−ϑ, ϑ),from Fig4:

ϑ = arctan

(a

2p

)

(12)

where, p is the distance between point X and the cellboundary and a is the hexagon side length. The time thatthe mobile terminal takes to move out of coverage whenmoving in the direction Θ ∈ (−ϑ, ϑ) is:

t =p sec Θ

v(13)

The probability of a mobile terminal handing off in atime less than τ is:

pho =

1 τ >

√a2

4+ p2

v

≈1

ϑarccos

( p

vτ

) p

v< τ <

√a2

4+ p2

v0 τ ≤

p

v(14)

On the other hand IntraC−B and IntraB−C handoverproposes a challenge to be estimated. To solve this,in terms of estimating the IntraB−C where the user isexpected to hand over from the parent macrocell to thecloud node in its domain, we consider the user to be locatedat a phantom cell in its’ location and therefore we areable to estimate the handover probability of the user tothe neighbouring cloud cell. On the other hand, to estimatethe IntraC−B , we consider that the user is handing overto a phantom cell located at the closest side of the cloudnode to the user, thus we are able to estimate the handoverprobability. The phantom cell would serve as a means toestimate the inter handover probabilities as shown in Fig5.At this point averaging the overall handover probability

Figure 5. The use of phantom cell to estimate the handoverprobability.

would yield the estimated handover probability of a userterminal.

The Therefore, if a user is allocated to a cloud node:

pHC = mean (IntraC−C , InterC−B , IntraC−B) (15)

where InterC−B = InterC−phantom. On the other hand, ifthe user is allocated to the macro station

pHB = mean (IntraB−C , InterB−B) (16)

where InterB−C = Intrephantom−C .

5.2.3. Power ConsumptionThe power consumption of a base station is not constant

but varies depending on the actual real-time traffic load.The main component power consumption that scales withtraffic is the PA DC power consumption. This is dueto the fact that in an idle mode the number of loadedsubcarriers transmitted is reduced and thus the power isless and/or some subframes are free of data due to thereduced traffic load [33]. Moreover the baseband processorpower consumption scales with traffic due to the fact thatwhen there are fewer users there are fewer subcarriers to beprocessed. On the other hand, power consumption scalingin small base stations is less significant, as the PA accountsfor 30% or less of the total power consumption.

The relation between the output power Pmax and thebase station power consumption Pc is nearly linear and thebase station power model can be approximated as follows[34]:

Pc =

{NT P0 + ΔP Pout 0 < Pout ≤ Pmax

NT Psleep Pout = 0(17)

where P0 is the power consumption at the minimum non-zero output power, Pout is the RF output power, ΔP isthe slope of the load-dependent power consumption andNT is the number of transceiver chains. The parameters


Table II. Parameters of the linear power model.

BS type NT RX Pmax W P0 W ΔP Psleep W

Macro 6 20.0 130.0 4.7 75.0

Micro 2 6.3 56.0 2.6 39.0

Pico 2 0.13 6.8 4.0 4.3

Femto 2 0.05 4.8 8.0 2.9

4.8 9.6 14.4 19.2 24 28.90.24

0.245

0.25

0.255

0.26

0.265

0.27

0.275

0.28

Pow

er C

onsu

mpt

ion

per

Are

a U

nit [

kW/k

m2]

Traffc Load per Area Unit [Mbps/km2]

Figure 6. Macro base station traffic load vs. power consumption[35].

of the linear power model for the considered BS types arelisted in Table II. As the macro stations are most affectedby the traffic load variation, it is important to representthis variation in the evaluation. Therefore, a system levelsimulation was conducted to derive the relation betweenthe traffic load and power consumption as shown in Fig.6.We considered a dense urban area in which the maximumtraffic load at peak hours was assumed to be 30mbs/km2.From Fig.6 we are able to estimate the power consumptionof a macrocell based on the instantaneous traffic load, asfor a given traffic load the macrocell would require a givenpower consumption.

6. EVALUATION AND DISCUSSION

System-level simulations were conducted to evaluate theperformance of the proposed solution. The aim was toobtain performance improvement figures in terms of theachievable blocking probability and power consumptionas well as to assess the impact on mobility in terms ofhandover probability. The performance indicators usedwere: (i) average user blocking probability, (ii) averageuser handover probability (i.e. the probability of a userhanding over, which reflects the possible mobility eventsoccurring in a cell) and (iii) overall power consumptionper cell. In order to model cloud cell based deploymentarchitecture as proposed in this paper, seven micro cells are

placed inside the area of a macrocell to simulate a densedeployment. We compare our proposed cloud cell baseddeployment with conventional macrocell deployment aswell as with only microcell based dense deployment.

6.1. Simulation Models and Assumptions

Models and assumptions are aligned with 3GPP simulationcase 1 [36]. Three-sector macrocells is simulated withwraparound with inter-site distance of 600m. The linkgain between the base station and a mobile is definedas the product of path loss, shadowing, and fast fadingeffects assumption given in Table III in lines with [37].A series of snapshot simulations are performed. In eachsimulation run, user terminals are randomly positionedwithin the coverage area. The radio link between a userterminal and macro cell or cloud node is calculated basedon the path-loss model. A log-normal shadow fading witha zero-mean and standard deviation of 6dB is assumed.The traffic model and mobility model parameters arepresented in Table III where a typical urban model isgiven. A dense urban area in which the maximum trafficload at peak hours was assumed to be 30Mbs/km2 in linewith the average daily data traffic profile of Europe asgiven in [38] is considered, resulting in a maximum of30 users to accommodate the traffic. Three scenarios ofcloud cells were studied with three different preferencevalues, generating different weights. The weights are setin a way to reflect various preferences, preferring a singleKPI to others for simulation purposes. On the otherhand, in practice the weights would be generated fromthe operator preferences, which are at the AHP weightgenerator. The achievable spectral efficiency of a radio linkwas calculated using a table-based adaptive modulationand coding (AMC) along the lines of 3GPP LTE systemscheme [30].

6.2. Blocking and Handover ProbabilityPerformance

In this section we first compare blocking and handoverperformance of our proposed solution with the twoalternative deployment approaches i.e. macro and microbased deployments. The inter site distance (ISD) of themacrocell deployment is 600m and the relation betweenthe two deployments is Dmicro = 2Rmacro/3 wherethe ISD of the microcell deployment is then 133.3m.Fig. 7 and 8 represents the blocking and handoverprobability performance of several deployment strategiesthereby reflecting their ability to service current trafficdemand and its ability to handle further requirementsof traffic. As expected the small-cell strategy achieves abetter performance level but at the cost of much higherhandover probability, reflecting a larger signalling demandand greater occurrence of mobility events. Whereas, formacrocell based deployment the trend is the opposite. Onthe other hand, as can be observed, the cloud-cell strategymerges the benefits of both and opens new regions forthe system to operate in. Also, by simply adapting the



Table III. Main simulation parameters.

Parameters Values

macrocell ISD ISD = 600 m

Shadow Fading Log-normal

6 dB standard deviation

Path loss PL= 131.1 + 41.8 log(d) (dB)

d = distance in km

Cell structure Hexagonal grid

of 3-sector sites

Micro base station P0 = 56.0 W

Micro base station 4p = 2.6

Micro base station Pmax = 6.3 W

Micro base station NT EX = 3

Channel bandwidths 10 MHz

Maximum users to be admitted U = 20

Maximum data connections/user D = 10

User data connections λd = 12 connection/sec

arriving rate

User service time mean value Th = 0.1017 sec

Information transferred R = 2 Mbits

mean value

Maximum cell load 30 Mbits/km2

Blocking probability weight χ ω(P b,χ) = 0.35

Handover probability weight χ ω(P h,χ) = 0.43

Power consumption weight χ ω(P c,χ) = 0.22

Blocking probability weight β ω(P b,β) = 0.2

Handover probability weight β ω(P h,β) = 0.6

Power consumption weight at β ω(P c,β) = 0.2

Blocking probability weight ζ ω(P b,ζ) = 0.6

Handover probability weight ζ ω(P h,ζ) = 0.2

Power consumption weight ζ ω(P c,ζ) = 0.2

Measurement internal τ 15 sec

User terminal maximum speed vmax 1.4 m/s

User terminal location X random distribution

in macrocell area

preferences in the AHP weight generator the system cantarget a better cell mean handover or blocking probability,providing large flexibility in the operational region.Furthermore, the cloud cell approach outperforms thedense micro deployment in terms of handover probabilitywhilst maintaining a high level of blocking probabilityperformance by intelligently allocating users to the mostappropriate base stations as well as having the ability tooffload the most demanding users in terms of the expectedhandover probability (such as high velocity users) to theoverlay macrocell to achieve a targeted goal. Since cloudcoverage solution provides the ability to assign weights tothe KPIs it provides high flexibility in terms of which KPIgets the priority. As can be seen, using the β weight setprovides higher performance levels in terms of mean userhandover probability and using the weigh set ζ providesa much lower achievable blocking probability. Although ζ

surpass the pure-micro deployment in terms of handoverprobability, it is due to the specific designed fuzzifier andcan be avoided by either adopting the β or χ weights oradjusting the membership values in the fuzzifier.

5 10 15 20 25 300

0.1

0.2

0.3

0.4

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0.7

Number of users in a cell

Ave

rag

e b

lock

ing

pro

bab

ility

per

cel

l

Pure-Macro deployment

Pure-Micro deployment

Cloud-cell deployment with β weights

Cloud-cell deployment with ζ weights

Cloud-cell deployment with χ weights

Figure 7. Blocking probability performance of several deploy-ment strategies.

5 10 15 20 25 300.1

0.15

0.2

0.25

0.3

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0.4

0.45

0.5

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rag

e h

and

ove

r p

rob

abili

ty p

er c

ell

Pure−Macro deploymentPure−Micro deploymentCloud−cell deployment with β weights

Cloud−cell deployment with ζ weights

Cloud−cell deployment with χ weights


Figure 8. Handover probability performance of several deploy-ment strategies.

6.3. Power Consumption Performance

As seen previously a dense deployment provides a higherlevel of performance in comparison to a large deploymentmethod at the expense of more mobility events. This makesa dense deployment more viable in areas where slow usersare expected, on the other hand, when considering thepower consumption values it reveals a larger expense ofusing dense deployment. As seen in Fig.9 the impact onthe network power consumption increases dramaticallywhen adopting a pure dense deployment compared tolarge area deployment (i.e. macrocell deployment). On thecontrary, the cloud cell approach reduces this margin interms of power consumption whilst maintaining a highlevel of performance, providing a way to increase system


performance without a large impact on the network powerconsumption. Therefore for further network densification,the cloud cell approach provides much more benefitsin terms of blocking, handover probabilities and powerconsumption.

5 10 15 20 25 30

200

300

400

500

600

700

800

900

Number of user in a cell

Po

wer

co

nsu

mp

tio

n p

er c

ell a

rea

Pure-Macro deploymentPure-Micro deploymentCloud-cell deployment with β weights

Cloud-cell deployment with ζ weights

Cloud-cell deployment with χ weights

Figure 9. Cell power consumption of several deploymentstrategies.

As cloud nodes are not required to provide anyservices, only awaiting activation by the macrocell in itsdomain, they can achieve a lower power consumptionin comparison with the conventional sleep mode (i.e.in a mode consuming less energy since only signallingis required). The available mode provides savings inboth high- and low-traffic periods in comparison withthe conventional sleep mode. Fig.10 provides an ideaof the amount of energy savings that can be achievedwhen deploying the proposed cloud cell based deploymentarchitecture. This saving becomes highly important andis substantial when the density (number of cloud nodes)of deployment Increases over-time to further counter theannual increase in traffic.

5 10 15 20 25 30200

300

400

500

600

700

800

900

Number of user in a cell

Po

wer

co

nsu

mp

tio

n p

er c

ell a

rea

Cloud-cell deployment with ζ weights (Sleep mode)Cloud-cell deployment with ζ weights (Available mode)

Figure 10. Cell power consumption of several deploymentstrategies.

6.4. Dynamics of the Network

The dynamics of the network can be observed basedon the changes of the environment, i.e. user location,speed, traffic demand and so on in Fig.11. Therefore,the density of the cloud node changes based on currentdemand levels and performance expectations. Moreover,Fig.12 represents the mean active number of cloud nodes ina cell representing the adaptivity of the network based onthe traffic demand changes. As can be observed as trafficdemand grows the number of active nodes increases tocontain this increase in demand, thereby aiming to providehigher level of performance. The activity of individualcloud nodes is managed by the parent macro cell therebybecoming available on demand taking into account theimpact on each individual KPI.

1 2 3 4 5 6 70

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Number of active cloud nodes in a cell

cdf

Low traffic demand Medium traffic demandHigh traffic demand

Figure 11. CDF plot of the active cloud nodes in a cell for weightset χ .

2 6 10 14 18 22 26 301.5

2

2.5

3

3.5

4

4.5


Ave

rag

e ac

tive

no

des

in a

cel

l

Cloud-cell deployment with β weightsCloud-cell deployment with ζ weightsCloud-cell deployment with χ weights

Figure 12. Mean active cloud nodes in a cell over trafficdemand.



7. CONCLUSIONS

In this paper we presented an alternative deploymentsolution for densification of future cellular networks inorder to meet the future traffic demands. This solutionbuilds on a notion of cloud coverage provided by denselydeployed self organizing small cells that can underlay themacro cells. The core idea of the proposed solutions is that,through a fuzzy-logic based decision framework presentedin the paper, the macro cells can control when to activatethe cloud cells, while taking into account a number offactors of cellular eco-systems e.g. traffic, energy tariffsetc. We have evaluated the proposed framework throughextensive simulations while using blocking probability,hand over probability and energy consumptions as KPIs ofinterest and compared it with both micro and macro cellbased deployments. The key advantage of the proposedsolution is that it combines the benefits of both macroand micro cell based deployments. However, unlikeconventional heterogeneous network, these advantages arenot gained at the cost of increased energy consumption orhigher handover rates, due to underlying fuzzy-logic basedself organizing solution that adapts the active deploymenttopology according to the spatial and temporal dynamicsof traffic while targeting the desired KPIs as set by theoperator. Although, only a specific number selected KPIsare considered in this paper, the proposed framework isexpandable to number of other KPIs of interest for futurework.

In cloud coverage there is not a single radio access unitfor a given user terminal but several options are given onwhich the network would conduct its decision. Thereby,the coverage of a cell is considered to be cloud coverage asthe coverage provided by these small cells can effectivelyfollow the user. On the other hand, cloud radio accessnetworks (C-RAN) proposes migrating the baseband units(BBUs) to the cloud for centralized processing, therebyseparating it from the radio access units (RAUs) [39,40, 41]. This approach provides several advantages ascompared to the conventional RANs, as C-RAN allowsfor the ability of centralizing the operation of BBUs andscalability it terms of the deployment of small cells asremote radio head (RRH). This migration of processingfrom the base station to a separated unit would provideseveral benefits for the proposed cloud coverage, as thecloud cell is required to generate a decision on whichof the small nodes are active and which are not. Thisdecision can be formulated at a separated unit as in C-RAN. Similarly to C-RAN the base station of the parentRAU collects the necessary metrics to determine the statusof the KPIs and would issue a request for a decision tobe formulated. Thereby, separating the functionality of theDCE to take part at a cloud-processing unit. Also, havingthis centralized processing unit provide an advantage interms of flexibility of further deployment of cloud cells.

ACKNOWLEDGEMENT

The author Talal Alsedairy is supported by King AbdulazizCity for Science and Technology. This work was madepossible by NPRP grant No. 5-1047-2?437 from theQatar National Research Fund (a member of The QatarFoundation). The statements made herein are solely theresponsibility of the authors.

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