greennets whitepaper

White Paper

Authors: Dr. Andreas Eisenbltter, Rafa Pisz and Szymon Stefaski

GreenNets White Paper

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Executive Summary The report Energy-saving solutions helping mobile operators meet commercial and sustainability goals worldwide (Ericsson, 2008) and the article Why the Telecom Sector Should Take Renewables Seriously (Greentechmedia, 2013) both state that energy costs account for as much as half of a mobile operators operating expenses these days.

Radio network solutions that improve energy-efficiency are not only good for the environment; they also make commercial sense for operators and support sustainable, profitable business on mature market. The last statement is becoming even more important in the context of forecasts such as Ericssons Energy and Carbon Report (Ericsson, 2013), which predicts the total electricity consumption of mobile networks, including future wireless access points, to triple by 2020 in comparison to 2007.

The challenge has been recognised by the industry. Researchers, standard organisations and hardware vendors are successfully working on improved solutions such as better or smarter hardware, better software or communication protocols. All of this will contribute to saving energy in the future once introduced into the market at large scale. The GreenNets approach, in contract, is different.

The GreenNets project has looked into what can be done to save energy NOW. With the aim of cutting the energy consumption of GSM, UMTS and LTE radio access networks at least 10%, the project consortium focused on vendor agnostic approaches optimizing network operation. This strategy is based on two main observations:

Current networks are typically designed and operated to secure coverage and capacity resources needed to deliver services in peak hours

Energy consumption hasnt been considered so far as radio access network optimization criterion.

The consortium has developed energy saving methodologies allowing for:

Switching-off temporarily unnecessary (pieces of) equipment (e.g. GSM TRXs or UMTS cells), matching capacity to demand while keeping coverage in a multi-RAT environment and not influencing the network topology.

Thinning out and optimizing topology of a single-RAT network by reconfiguration of antennas tilts, profiting from the same fact as described above.

The underlying idea of the proposed methods is to exploit coverage redundancies in order to adapt the network configuration to better match the actual service demands of the network. In such deployments there are usually in use technologies of different generations (2G, 3G, 4G) and different hierarchical levels (macro-, micro-, pico-, femto cells) with overlapping coverage. If the service demands can be fulfilled by different subsets of network elements the energy savings can be significant when configuring the network to having only one of those subsets of network elements active.

GreenNets solutions have been tested on the real network data. Implementing the first method in a single-RAT setting, just switching off redundant capacity, energy savings of up to 7.1% were uncovered. Moreover, such savings can be achieved within the current hardware and software landscape of mobile network operator. Designed as a functional extension of current planning tools and OSSs, the GreenNets software components can be easily fused with the planning, maintenance or optimisation processes as they use easily available performance and configuration data and are capable of automated implementation of changes in the network configuration (centralised SON) communicating with standard OSS interfaces.


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Introduction During the past three years there are clear trends that energy consumption reduction became a strategic goal for mobile network operators (MNO) around the globe. Different sources name a different numbers of potential financial losses, waste of energy or CO2 footprint. But all of the sources prove the critical need of improvements in the area of mobile network energy consumption optimisation.

According to press information report Energy-saving solutions helping mobile operators meet commercial and sustainability goals worldwide (Ericsson, 2008) and the article Why the Telecom Sector Should Take Renewables Seriously (Greentechmedia, 2013) energy costs account for as much as half of a mobile operators operating expenses. Additionally Ericssons Energy and Carbon Report (Ericsson, 2013) forecasts the total electricity consumption of the ICT sector to increase by almost 60% from 2007 to 2020 owing to the increasing number of devices and network expansion. As for the electricity consumption of mobile networks, including future wireless access points, is expected to not more than triple by 2020.

Thus radio network solutions that improve energy-efficiency are not only good for the environment; they also make commercial sense for operators and support sustainable, profitable business. There is a clear trend that functionality of mobile phones increases dramatically over the past two decades, transforming the mobile phone to a device capable of far more than simple voice calls. Market demands are expected to increase the bandwidth of the digital cellular network up to eight times over the next years. That is possible by installing additional equipment (e.g. 3G and LTE layers as capacity over existing 2G or 3G as coverage layers) or adding more sites with reduced heights and cell sizes, which will lead to even higher total energy consumption. The reports highlight the MNO focus on OPEX optimisation as well. Improvements must be achieved by complex solutions combining new generation of radio equipment amended with additional control solutions.

Another report published by Capgemini (2009) analyses cost reduction strategies for MNOs. Network operating expenditure is identified as one of the key areas for improvement, since operating costs take from 50% up to 75% of MNO revenue. The network operation expenses make up 23.7% of the overall OPEX and electricity costs make up 15% of the network OPEX. The findings in the Capgemini report are confirmed by the latest resources as well as, for example, the article Saving Operating Expenses in the Mobile Backhaul (Juniper, 2013). This report states that the network operating costs are even higher and reach 30%, since the expansion of the 4G/LTE services requires a substantial increase in the network bandwidth in terms of extra equipment and power consumption.

Leading network equipment providers (NEP), supplying MNOs around the globe, are proactively tackling the reduction of energy consumption too. They are running their own economy studies and technology development, announcing the corporate commitment for the energy and CO2 footprint reduction and developing modern energy effective offerings. Numerous initiatives and solutions have been emerging over the last 12-24 months, and there is no unified trend how to address the problem at the moment.

The GreenNets approach is different. The consortium decided to look for solutions, which allow saving energy and reducing CO2 emission in short term without depending on changes to the hardware infrastructure. The products developed by the GreenNets Consortium are able to recognize when and which resources are in excess due to (temporarily) network over-dimensioning, and then propose a new configuration for network parameters adequate to the actual demand on services. As energy-efficient network configuration settings depend on variable network states, they need to be applied and adapted dynamically. This dynamic approach allows the GreenNets products to adjust network capacity to users demand on services and to save resources.


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The consortium has developed energy saving methodologies allowing for:

Switching-off temporarily unnecessary (pieces of) equipment (e.g. GSM TRXs or UMTS cells), matching capacity to demand while keeping coverage in a multi-RAT environment and not influencing the network topology.

Thinning out and optimizing topology of a single-RAT network by reconfiguration of antennas tilts, profiting from the same fact as described above.

The underlying idea of the proposed methods is to exploit coverage redundancies in order to adapt the network configuration to better match the actual service demands of the network. In such deployments there are usually in use technologies of different generations (2G, 3G, 4G) and different hierarchical levels (macro-, micro-, pico-, femto cells) with overlapping coverage. If the service demands can be fulfilled by different subsets of network elements the energy savings can be significant when configuring the network to having only one of those subsets of network elements active.

This White Paper is focused on the first method, since every mobile network operator could integrate the GreenNets software solution implementing the approach Energy Efficiency Optimiser within few weeks.

This paper is organized as follows:

The first section provides the business context of energy saving in mobile networks an analysis of potential savings for two different networks is made

The second section presents the Energy Efficiency Optimizer (EEO) a tool developed by the GreenNets Consortium and sketches how it is forecasting traffic and optimizing network configuration to save energy

The last part includes an analysis of potential savings achieved by using EEO in a real network


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Business potentials The GreenNets Consortium has performed an analysis of business potentials in terms of saving energy in mobile communication environment and determined the most promising domains. The analysis was done based on performance and configuration data from two different networks in two different countries, at different stages of development and composed of legacy devices as well as latest technologies. The Consortium conducted the analysis using the Business Case Demonstrator (BCD) a tool developed by the Consortium that is capable of digesting data on an operators network configuration, the equipment in use, traffic profiles per cell, as well as power contracts (per site).

The network infrastructure of Mobile operator 1 in Country 1 (M1) is based on traditional solutions: one core network and separate RANs for 2G and 3G. Mobile operator 2 in Country 2 (M2) operates a significantly smaller network built on more modern technologies like Software Defined Radio Base Stations (SDR BTS). These different mobile network realizations allowed us to perform analysis from different perspectives. First analysis was focused on network elements and energy consumption domains. The results show that beside the different absolute values in both networks, the base station sites are the main power consumers and cover almost 99% of network power consumption. In the next stage, power consumption of different types of base stations was investigated and a simple model of energy consumption was built. The investigation shows a correlation between the type of equipment used by an operator (old vs. modern) and the energy consumption level, as the base stations deployed by M2 network consume 40 % less energy.

The analysis of the GreenNets Consortium focused on traffic profiles. The pattern of daily voice traffic for two separate geographical areas with 200 base stations each is presented in Figure 1.

Analyzing daily traffic profiles reveals a possibility of saving energy by efficient management of free capacity and adjusting network resources automatically as traffic increases or decreases during the day. The operators could switch off some segments of the site or capacity oriented network elements, as each cell in a traditional mobile network is designed with a maximum capacity to carry at least 100% of the

Figure 1 Daily traffic profiles (Monday)


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peak traffic. During off-peak times, almost all base stations operate at low load and with relatively low output powers, where the efficiency of the PA (power amplifier) and the base stations is very poor. In that sense, spare capacity should be avoided and the capacity should be adapted to the demand over time.

Switching OFF excessive capacity network elements when traffic is low is the main method of saving energy in mobile communication networks developed within the GreenNets project

To check the efficiency of the approach, a calculation of energy consumed by both M1 and M2 networks have been performed. For the purposes of the analysis it was assumed that all redundant radio units, i.e. radio units without any impact on the current network quality, are switched off (Switch-off abundant capacity method developed by the GreenNets Consortium).

The following assumptions were made for the calculation of possible energy savings and the economic impact:

1. Average power consumption per site 2833W for M1, and 1682W for M2 2. Network size 1450 (M1), 865 (M2) of GSM and UMTS sites

Figure 2 and Figure 3 show the average load for each RAT in both networks and the total power consumption calculated for the reference case and after the application of Switch-off method. It should be noted that the working day/weekend distinction is not the only factor influencing the load pattern. Differences between areas of network deployment urban, suburban or rural are also taken into consideration.

Figure 2 Modeled Load and Power Consumption profile for M1, generated by BCD


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Figure 3 Modeled Load and Power Consumption profile for M2, generated by BCD

The difference between the total power consumption profiles in the reference case and the switch-off cases indicates potential energy savings over the time. It is evident that most savings can be achieved at night time when users activity is lower.

Table 1 and Table 2 present total energy consumption by different time periods for M1 and M2, working in normal operation mode (Normal Operation) and after application of GreenNets approach (Switch-Off). The difference is about 10% in both cases.

Table 1 Energy consumed by M1 network (estimation generated using BCD)

Table 2 Energy consumed by M2 network (estimation generated using BCD)

per Day per Month (31 d) per Year (365 d)Normal operation 90 437.37 kWh 2 803 558.49 kWh 33 009 640.25 kWh

Backhaul/ Site management 3 572.06 kWh 110 733.88 kWh 1 303 802.09 kWhSwitch-Off 80 499.65 kWh 2 495 489.10 kWh 29 382 371.68 kWh

Savings 9 937.72 kWh 308 069.39 kWh 3 627 268.57 kWh

Energy Consumption [kWh]

per Day per Month (31 d) per Year (365 d)Normal operation 26 405.64 kWh 818 574.84 kWh 9 638 058.61 kWh

Backhaul/ Site management 2 122.61 kWh 65 800.79 kWh 774 751.26 kWhSwitch-Off 23 801.04 kWh 737 832.12 kWh 8 687 378.17 kWh

Savings 2 604.60 kWh 80 742.72 kWh 950 680.45 kWh

Energy Consumption [kWh]


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Energy Efficiency Optimizer The GreenNets Energy Efficiency Optimizer (EEO) is a centralized SON solution that automatically computes reconfigurations of a multi-RAT (GSM, UMTS, and LTE) cellular network in order to minimize energy costs in an on-line control setting. EEO implements the GreenNets approach to switch off excessive capacity network elements when traffic is low.

EEO analyses performance and configuration data of an operating network, coming from the northbound interface (Itf-N) of the network Operating Support System (OSS), automatically detects the areas of inefficient energy use and calculates a new setup of the network parameters adapting network capacity to the forecasted demands. The adjustments proposed by EEO are based on observation of historical data acquired from the network OSS and their projection into the future. EEO is a vendor agnostic solution and operates on top of a mobile networks OSS.

EEO has been implemented as an autonomic manager, continuously performing the following activities:

1. Monitoring: Collecting information about the network and checking whether various performance metrics are within the ranges allowed by the operator

2. Analyzing: Making predictions for various characteristics, such as coverage of the cells and traffic

3. Planning: Utilizing various optimization techniques to compute a plan comprising actions whose execution at specified times should improve energy efficiency of the network without degrading its performance

4. Executing: Performing the planned actions

For a smooth realization of these activities, the implementation of EEO is split into two functional parts, each running independently in its own thread: a Computational Module and a Configuration Module, cf. Figure 4. These parts communicate using persistent storage. Roughly speaking, the Computational Module computes and saves the plan for execution in EEOs internal database (DB), while the Configuration Module implements this plan at a later stage by sending the appropriate updates to the network via Data Access Layer (DAL). There is a fallback state defined for each network element (defaulting to this element being turned on). In emergency situations, i.e. when the implemented changes impair the network performance or when there is no plan computed for the particular part of the optimized region, the affected network elements are reverted to the fallback state. Hence, the role of the Configuration Module is to Monitor and Execute.

Traffic Forecasting Module The Traffic Forecasting Module is one of the main components of Computational Module. The objective of traffic forecasting is to predict traffic patterns for a given day and time within a cell (or a group of cells) and combine it with geographical information and users distribution model.

The process has been depicted in Figure 5. The forecasting module acquires environmental data (Geo-Data) from an external geo-information source and derives raster data for land-use classes and roads. In a combination with Network Configuration data and path loss predictions, Cell Assignment Probability maps for the respective network are generated. Subsequently, Traffic Forecasting for individual cells is performed. Based on machine learning techniques, two different algorithms for forecasting voice and data traffic are used. The algorithms incorporate prior information about the regularity of the time series and use contextual information such as information about holidays and weekends for voice traffic


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prediction and statistics like quintile estimations for packet data traffic. In case of voice traffic, its pattern is predicted; while for data traffic, the upper end of a confidence interval is estimated.

Figure 4 Software components of the Energy Efficiency Optimizer

The predicted traffic is then mapped onto the respective cell area and distributed among the prevailing land-use classes in this area, depending on the day of the week and the time of the day (e.g. most users stay at home at night, whereas in the morning, most users are on the road). The temporal and spatial user and load distributions are formed into a set of Traffic Intensity maps, which indicate where and when the forecasted demands for the network and its resources will exist. An example of a traffic map is presented in Figure 8.

Optimization Engine The Optimization Engine performs an analysis of traffic intensity forecasts and searches for optimal set of changes to be performed in the network to save energy without unduly degrading the quality of service experienced by users.


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Figure 6 Voice traffic, observed in the network and predicted with EEO

Figure 5 Traffic forecasting process in EEO In the first step, the Forecasting module acquires environmental data (Geo-Data) from an external geo-information source and transforms them into clutter data. Next, the Cell Assignment Probability function

quantifies associations of the pixels and covering cells. In the third step, Traffic Forecasting algorithms predict resources utilization at each cell. At the end of the process, Traffic Intensity Maps that represent the forecasted traffic distributed over time and space are composed of traffic forecasts and user distribution function, Cell

Assignment Probability maps and Land-use Maps.


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Figure 8 Traffic maps generated with EEO Consecutive pictures illustrate traffic at different time of the day.

High traffic areas are depicted with light colors.

Figure 8 Packet traffic, observed in the network and predicted with EEO


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In terms of radio access technology the Optimisation Engine is looking for temporarily unnecessary pieces of equipment (e.g. GSM TRXs or UMTS cells), which could be switched off, matching capacity to demand while keeping coverage in an multi-RAT environment and not influencing the network topology.

In a mathematical sense, the Optimization Engine solves a combinatorial optimization problem consisting of selecting the set of network elements with the smallest sum energy consumption, constrained by the QoS requirements from users and network.

The underlying idea of the proposed methods is to exploit coverage redundancies in order to adapt the network configuration to better match the actual service demands of the network. In such deployments, there is usually dedicated equipment for different generations (2G, 3G, 4G) and different hierarchical levels (macro-, micro-, pico-, femto cells) with overlapping coverage. If the service demands can be fulfilled by different subsets of network elements the energy savings can be significant when configuring the network to having only one of those subsets of network elements active.

It should be emphasised that changes of network configuration computed by the Optimization Engine could be implemented in a short time, to let profit even from quite short periods of lower traffic.

Figure 9 Network optimization according to the forecasted traffic generated in EEO. Consecutive pictures illustrate traffic in the network and optimized network configuration at different time of

the day. At the time of the low traffic (between 1:00 am and 5:00 am), a large number of the red points indicate network elements selected as a subject for optimization (switching-off). At daytime up to the midnight, network

configuration is adapted to the higher traffic and only very few network elements could be switched off

Examples of different network configurations results of computation done by the Optimization Engine are presented in Figure 9. The consecutive pictures illustrate traffic in a network and the optimized network configuration at different times of the day. At the time of low traffic (between 1:00 am and


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5:00 am), a large number of red points indicate NEs selected for optimization (switching off). During daytime up to midnight, the network configuration is adapted to the higher traffic and only very few NEs could be switched off.


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Real network analysis The GreenNets EEO has been tested on real data obtained from a European operator. The environment where the data was collected covers urban, suburban as well as rural areas and includes cities of different sizes. The analyzed network consisted of 361 3G sectors (311 with 2 carriers and 50 sectors with 3 carriers) and 787 2G cells (639 with 2 TRX per sector, 115 with 3 TRX per sector and 33 with 4 TRX per sector).

We assumed a conservative approach with capacity adaptation, separately for each RAT, only, which means that only the capacity elements were switched off (in case of GSM sectors - redundant TRXs and in case of UMTS sectors - redundant carriers).

2G network Figure 11 shows how many resources EEO operating on the network could save, by switching off one or more TRX, without impacting the coverage of the GSM network.

Figure 10 Unused resources in GSM network (real network analysis)

3G network Figure 12 shows how many resources EEO could save, by switching off one or more carrier, without impacting the coverage of the UMTS network.

Energy savings The presented results show that assuming a very conservative approach, EEO could optimize energy consumed by 88% of the GSM cells and all UMTS sectors. In all cases, at least 5% of the resources could be saved, which means that at least one TRX or one carrier could be switched off for ca. 2 hours. In case of the analyzed GSM network, approximately 14% of the resources could be saved (switched off). For the UMTS, part of the results are even better 21%.


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Figure 11 Unused resources in UMTS network (real network analysis)

Table 3 presents the results expressed in kWh of saved energy - calculation has been done using BCD. It shows that EEO running in the most conservative mode could optimize energy consumption by 5.4%, for the whole analyzed network and 4.13% and 7.33% for UMTS and GSM, respectively.

Normal'Operation

Savings'with'EEO Per'Technology Total

Cells'per'Sector #'Sectors2 311 '''''''3'191,32''''' ''''''''1'137,58'''''3 50 '''''''3'614,72''''' ''''''''1'794,58'''''

'''''''3'249,96''''' ''''''''1'228,58'''''TRX'per'Cell #'Sectors

2 639 '''''''2'489,30''''' ''''''''''479,37'''''3 115 '''''''2'698,57''''' ''''''''''778,67'''''4 33 '''''''2'939,47''''' ''''''''1'048,77'''''

'''''''2'538,76''''' ''''''''''546,98'''''

Network

UMTS

GSM

Energy,Savings,per,Network,Energy,Consumption,per,Cell,Calculated,Using,BCD,Model,

(kWh)

5,40%

159'162,24'kWh

Weighted'Mean

Weighted'Mean

73'130,89'kWh

4,13%

86'031,35'kWH

7,33%

Table 3 Energy savings with EEO


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Conclusions As the analysis of the real case has shown the GreenNets Consortium developed a solution that could efficiently reduce energy consumed by the present radio access networks.

Designed as a functional extension to current OSSs, the GreenNets software components can be easily fused with the maintenance or optimisation processes. The software components use easily available performance and configuration data and are capable of automated implementation of changes in the network configuration (centralised SON) communicating with standard OSS interfaces.

Within few weeks time, every mobile network operator could start cutting its energy costs and positively influencing the environment - reducing the CO2 emission and electro-smog - without investments in new hardware or deployment of new technology, just implementing dynamic approach to network configuration developed by GreenNets.


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About GreenNets The GreenNets Project Power consumption and CO2 footprint reduction in mobile networks by advanced automated network management approaches is an R&D initiative of three SMEs and three research institutes from Germany, Poland and Lithuania. The research has received funding from the European Union, Seventh Framework Programme (FP7/2007-2013) under Grant Agreement n 286822.

The GreenNets Partners are:

atesio GmbH BENCO Baltic Engineering Company UAB DATAX Sp. z o.o. Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Berlin Technical University of Braunschweig, Institute for Communications Technology University of Wrocaw, Institute of Computer Science

Contributors to the White Paper are:

atesio GmbH Dr. Andreas Eisenbltter, Dr. Ulrich Trke, BENCO UAB Darius Montvila, Riardas Branas, ydrn Vitait DATAX Sp. z o.o. Dr. Krystyna Napieraa, Szymon Stefaski, Dr. Radosaw Czopnik, ukasz

Krajna, Rafa Pisz, Krystian Sroka Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute Prof. Dr. Sawomir

Staczak, Dr. Renato G. Cavalcante; Dr. Federico Penna, Dr. Jrg Bhler, Emmanuel Pollakis Technical University of Braunschweig, Institute for Communications Technology Prof. Dr.

Thomas Krner; Johannes Baumgarten; Dennis M. Rose University of Wrocaw, Institute of Computer Sciences Dr. Rafa Nowak, Dr. Marcin Biekowski,

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