INTRODUCTION

With increasing awareness of the potential harm-ful effects to the environment caused by CO2emissions and the depletion of non-renewableenergy sources, there is a growing consensus onthe need to develop more energy-efficienttelecommunication systems. It has been estimat-ed that 3 percent of the world’s annual electricalenergy consumption and 2 percent of CO2 emis-sions are caused by the information and commu-nication technology (ICT) infrastructure.Moreover, it is estimated that ICT energy con-sumption is rising at 15–20 percent per year,doubling every five years [1]. According to oneestimate, about a tenth of this can be attributedto cellular mobile communication systems. As of2008, this corresponded to 60 billion kWh ofelectricity usage annually, about 40 million met-ric tons of CO2 emissions each year. To put it inperspective this is equivalent to annual green-house gas emissions from about 8 million cars.Another consistent estimate states that therewere over 600 thousand base stations in China

deployed by three major operators which con-sumed about 20 billion kWh in 2007.

From the perspective of cellular network opera-tors, reducing electrical energy consumption is notonly a matter of being green and responsible, it isalso very much an economically important issue. Asignificant portion of the operational expenditure(OPEX) of a cellular network goes to pay the elec-tricity bill. From the above, it can be estimated thatthe mobile network OPEX for electricity globally ismore than $10 billion dollars today.

For all these reasons, cellular network operatorshave been exploring ways to increase energy effi-ciency in all components of cellular networks,including mobile devices, base stations, and core(backhaul) networks. There has been a tremendousamount of work on mobile device energy efficiencywith the objective of prolonging battery life time.Similarly, green operation of Internet has been con-sidered and some of the techniques can be extend-ed to the cellular backhaul networks. However, thekey source of energy usage in cellular networks isthe operation of base station equipment. It hasbeen estimated that base stations contribute to60–80 percent of the total energy consumption [2].In this article, we therefore focus on energy-effi-cient operation of cellular base stations, which isreferred to as green cellular operation.

Energy efficiency with respect to base stationshas been considered in all stages of cellular net-works, including hardware design and manufac-ture, deployment, and operation. A number ofthese efforts have focused on hardware improve-ments. For instance, next-generation base sta-tions are designed to be substantially moreenergy efficient, for example, using more energy-efficient power amplifiers and natural resourcesfor cooling. Others have considered collocatingcellular base stations with renewable energysources such as solar power and wind energy. Inaddition, cellular operators have evaluateddeployment strategies that minimize the energyexpenditure on base stations [3]. We primarilydiscuss operation, and touch on deployment.

There is room for significant improvement incellular operation. Even when a site is experienc-

IEEE Communications Magazine • June 201156 0163-6804/11/$25.00 © 2011 IEEE

ABSTRACT

The operation of cellular network infrastructureincurs significant electrical energy consumption.From the perspective of cellular network opera-tors, reducing this consumption is not only a mat-ter of showing environmental responsibility, butalso of substantially reducing their operationalexpenditure. We discuss how dynamic operation ofcellular base stations, in which redundant base sta-tions are switched off during periods of low trafficsuch as at night, can provide significant energy sav-ings. We quantitatively estimate these potentialsavings through a first-order analysis based on realcellular traffic traces and information regardingbase station locations in a part of Manchester,United Kingdom. We also discuss a number ofopen issues pertinent to implementing such ener-gy-efficient dynamic base station operationschemes, such as various approaches to ensure cov-erage, and interoperator coordination.

ENERGY EFFICIENCY IN COMMUNICATIONS

Eunsung Oh and Bhaskar Krishnamachari, University of Southern California

Xin Liu, University of California at Davis

Zhisheng Niu, Tsinghua University

Toward Dynamic Energy-EfficientOperation of Cellular NetworkInfrastructure

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IEEE Communications Magazine • June 2011 57

ing little or no activity, the base station consumesmore than 90 percent of its peak energy; forexample, a typical universal mobile telecommuni-cations system (UMTS) BS consumes 800–1500W and has a radio frequency (RF) output powerof 40–80 W. While turning off some of the radiotransceivers on a BS during low traffic periodscan provide some relief, this is still not sufficient.For significant energy savings, what is called foris a more carefully coordinated dynamic approachthat allows the system to shut entire base stationsand transfer the corresponding load to neighbor-ing cells during periods of low utilization.

In this article, we explore such dynamic basestation management mechanisms for cellular net-works to understand quantitatively the scope forpotential energy savings, and also the technicalchallenges that arise in implementing these mecha-nisms, particularly to ensure that energy efficiencydoes not come at the expense of reduced quality ofservice for mobile customers. Note that althoughsimilar approaches can also be considered forreducing the energy consumption in wireless localarea networks such as 802.11-based WLANs, wedo not explore these in this work as the electricalenergy usage and expense in such networks is anorder of magnitude lower than in cellular networks(e.g., Cisco estimates that its wireless control sys-tem adaptive power management software cansave 7 W/h per Aironet access point).

The article is organized as follows. We presenta first-order analysis on the scope of potentialenergy saving through green cellular operation.We use a simple greedy algorithm to estimate theenergy saving based on real cellular traffic tracesand actual base station location information inthe analysis. We discuss related challenges andpotential solutions in green cellular operation,including maintaining coverage, enabling cooper-ation between operators, and providing E911 ser-vice. Last, we conclude the article.

A CASE STUDYThe basic idea behind green cellular operation isto alleviate the inefficiencies resulting from thefact that today’s base stations are typicallydeployed and operated continuously based onpeak traffic estimates. Intuitively, it saves energyto carefully turn off unutilized and underutilizedbase stations during off-peak times, while main-taining coverage. Next, we use real cellular net-work data to estimate the degree of energysavings possible in urban areas using such adynamic base station operation.

We look at two sets of real data. The first is atemporal traffic trace shown in Fig. 1, which wasobtained from a cellular operator (the source hasto be anonymized to preserve confidentiality) in ametropolitan area; and the second is base stationlocation data from a central portion of the city ofManchester, United Kingdom, as illustrated in Fig.2, obtained from a UK government-sponsoredwebsite [4]. We discuss these traces in detail below.

TEMPORAL TRAFFIC TRACEThe data trace we have contains voice call infor-mation from five base stations — one central basestation and its neighboring four base stations —over the course of one week. The data is plotted

with a resolution of 1 s in Fig. 1. We normalizethe values of load by a constant value to show therelative change. We observe a periodic sinusoidalprofile of the traffic in each cell and notice thatthe traffic during the daytime (11 a.m.–9 p.m.) ismuch higher than that at nighttime (10 p.m.–9a.m.). In addition, the traffic profile during aweekend/holiday period, even during peak hours,is much lower than that of a normal weekday.

SPATIAL DEPLOYMENTFigure 2 is a snapshot of the base station loca-tions (shown as colored triangles) in part of thecity of Manchester, United Kingdom. This infor-

Figure 1. Normalized traffic profile during one week from real voice call infor-mation.

Time (hour)240

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fic

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ile

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48 72 96 120

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144 168

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Figure 2. Base station location data from a part of Manchester, United King-dom. (Some base stations are collocated, in which case just one triangle isshown on the map.)

X-distance (km)0.50

A56A34

A62

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1 1.5 2.0 2.5 3.0 3.5

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IEEE Communications Magazine • June 201158

mation is obtained from a U.K. governmentsponsored interactive website [4] based on infor-mation provided voluntarily by mobile networkoperators on the location and operating charac-teristics of individual base stations. The informa-tion on this site includes location information,operator information, maximum transmissionpower, and cellular tower height and operatingfrequency. The information is updated by opera-tors every three months or so [4]. We manuallyextract base station data from a part of Manch-ester in the United Kingdom from this website.This area is of size 3.5 × 3.5 km, and has a totalof 139 base stations in 128 locations.

ENERGY SAVING ESTIMATEWe now use these real datasets to estimate theenergy savings that are possible using dynamic basestation operation in this part of Manchester. Notethat the anonymized temporal trace data we havedoes not actually correspond to the base stationlocations in Manchester. However, our methodolo-gy for estimating the energy savings from dynamicbase station management decouples the temporaland spatial components, as follows. We use thetemporal traffic trace to generate rough estimatesof the fraction of time when cells show low activityduring weekday and weekend/holiday periods.Independently we analyze the spatial distributionof base stations (on a per-operator basis, as well as

jointly) assuming uniform circular coverage zonesto estimate the level of redundancy that can beeliminated while maintaining more than 95 percentof the coverage, for different communicationranges. We assume for now that the remaining 5percent of coverage can be made up through thekinds of techniques discussed later. We combinethese two intermediate estimates to generate thefinal estimate of the expected energy savings.

Based on the traffic load profiles shown in Fig.1, we obtain the percentage of time that the trafficis below x percent of weekday peak, during week-days and weekends, where x = 5, 10, 20. The num-bers are presented in Table 1. In the followingdiscussion, for ease of exposition, we shall focuson time when traffic is less than 10 percent of thepeak (further work is needed to better understandexactly what fraction of peak traffic should be cho-sen as the threshold for dynamic base stationoperation). We find that during weekdays, about30 percent of the time the traffic is less than 10percent of the peak. During weekends and holi-days, this low activity period increases to 43 per-cent of the time. Assuming two weekend days in atypical week, we estimate that the average lowactivity period is about 34 percent of the time.

We next consider the level of redundancy pre-sent in the spatial base station deployment. Asshown in Fig. 2, the deployment shows non-homo-geneous density for each operator, with greaterdensity near the city center, which is a primarilycommercial district. In the interest of a simplifiedfirst-order analysis, we assume that all cells have acommon maximum coverage range. (In order tominimize intercell interference, some base stationsmay be operated below this range when all basestations are activated. We assume that the range ofthese base stations can be extended to the maxi-mum level when necessary.) If all base stationswere to operate at maximum range, there wouldbe significant redundant overlap in coverage. Forinstance, Fig. 3 shows the coverage of all base sta-tions when a common range of 700 m is chosen foreach base station, with colors indicating the extentof redundancy. Near the city center, we find thatmore than 50 base stations from various operatorsoverlapping if operated at this maximum range.

We first treat each operator separately. For agiven maximum range, we use a greedy algorithmto identify a minimal number of base stationssuch that the net area coverage of these base sta-tions is more than 95 percent of the area coveredwhen all base stations are active. In the greedyalgorithm, we sequentially switch off the base sta-tion with the minimum distance to its nearestactive base station so long as the coverage condi-tion is met. (We discuss later how any remainingcoverage holes may be filled by techniques suchas range extension, multihop relay, and multipointcoordination). This is plotted in Fig. 4. As expect-ed, the level of redundancy is higher when themaximum range is higher. For example, when themaximum range is 700 m, depending on the oper-ator, from 25 to more than 65 percent of the basestations can be deactivated while maintainingmore than 95 percent of the original coverage.

We also plot in this figure the percentage ofbase stations that could be switched off whilepreserving 95 percent of the original coverage ifall operators were able to share the base station

Figure 3. Redundancy in the cellular coverage of the Manchester area at basestation coverage.

X-distance (km)0.50

0.5

0

Y-di

stan

ce (

km)

1

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2

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3

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5

0

10

15

20

25

30

35

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45

50

1 1.5 2 32.5 3.5

Table 1. Analysis of sample cellular traffic load profiles: percentage of time thetraffic is below x percent weekday peak during weekdays and weekends, for x= 5, 10, 20.

Threshold Weekday Weekend Average per week

5% of peak 23.2 29.8 25.1

10% of peak 30.2 43.3 34.0

20% of peak 38.6 75.6 49.2

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IEEE Communications Magazine • June 2011 59

resources. Clearly the overall energy savings arehighest in this case (note that from the datasetwe have, it appears that some sites are alreadyjointly shared by multiple operators; in this anal-ysis we have assumed that there is a single basestation shared by all active operators at suchsites). For the 700 m range, we observe thatmore than 85 percent of the base station can beshut down while keeping greater than 95 percentcoverage with minimal overlap in this case. Com-paring the total turned-off base stations in thecase when operators act individually, we see thatbase station sharing increases the energy savingby about 35 percent during off-peak hours.

Putting together the temporal and spatial anal-ysis above, we estimate that individual operatorscan save between about 8 percent to 22 percent ofenergy in such an urban deployment. Sharing basestation resources together, we get a total reductionof about 29 percent for the energy expended onbase station operation. Thus an important collat-eral finding of our analysis is that greater coopera-tion among operators is essential for substantialsavings. We shall discuss this issue further next.

This percentage of energy saving correspondsto between 32 and 60 kWh of absolute energy sav-ings for the roughly 12 km2 area of Manchester wehave considered (assuming the single base stationpower is between 800 and 1500 W). This in turntranslates to about $42,000 to $78,000 annually forthe electricity bill for this set of base stations, orabout 200 to 375 metric tons of annual CO2 emis-sions. This is a substantial reduction in greenhousegas emission as well as cost of operation.

Some caveats are in order. Our study has notconsidered heterogeneous networks. Also, an anal-ysis of savings requires better radio propagationmodeling. These can be considered in future work.

CHALLENGES ANDFUTURE DIRECTIONS

In the above analysis we focused on obtaining acoarse-grained estimate of energy savings if“redundant” base stations could be switched offduring periods of low activity. However, thisanalysis has glossed over many important detailspertaining to how such dynamic base stationoperation can be implemented in practice. Anumber of questions arise: What are the mecha-nisms by which coverage can be maintained whensubsets of the base stations are switched off?What changes may be needed to make the mobileunits more “cognitive” in order to enable moreagile base station management across multiplespectrum bands? What is the temporal granulari-ty at which base station operation decisionsshould be made? At what locality level of the cel-lular network hierarchical architecture shouldthese decisions be implemented? Is there reallyan incentive for different operators to cooperatein base station operation? If so, how can suchcooperation be realized? We now explore theseissues in greater detail, in the process identifyingavenues for future research and development inthis area. In particular, we focus on coverageextension, then address other technical issues,and consider the location estimation problemthat arises in some cellular deployments.

MAINTAINING COVERAGE USINGPOWER CONTROL

The primary constraint of green cellular opera-tion is to preserve coverage and service qualitywhen certain cellular base stations are turned offin the case of low load. Techniques for preserv-ing coverage and maintaining quality of servicehave much similarity. To be concise, we focus thefollowing discussion on maintaining coverage.

Power control will play an important role in bal-ancing coverage and interference. Power control hasbeen extensively used in cellular networks for inter-ference management and quality of service provi-sioning. In the case of low traffic load, the mainchallenge is to increase coverage, while interferencemanagement is less critical. Therefore, one canpotentially increase transmission power when somebase stations are turned off to increase the coveragearea of the remaining base stations. Although a sim-ple idea, it needs to be carefully evaluated for bothuplink and downlink transmissions.

COGNITIVE MULTIFREQUENCY OPERATIONMulti-frequency-band operation can potentiallybe explored in green operation. Lower frequencybands have better penetration capability and canprovide better coverage under the same transmis-sion power constraint. An example of such aband is the vacant 700 MHz TV band in theUnited States, auctioned in February 2009 andacquired mainly by large cellular operators. Sucha lower frequency band could potentially be usedby larger cells, overlapping with smaller cells on ahigher frequency band. It is worth noting thatfrequency selection requires more advancedphysical layer technology, which is available incurrent commercial mobile devices to somedegree, with more advanced features being devel-oped as part of the cognitive radio paradigm.

MULTIHOP RELAY FOR COVERAGE EXTENSIONVarious techniques have been considered in cel-lular networks to improve network coverage

Figure 4. Energy saving during off-peak times vs. the base station range in theManchester area.

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IEEE Communications Magazine • June 201160

and/or increase network throughput. Some ofthese techniques can be applied in the case ofgreen cellular operation with a focus on coveringthe cells with shut-down base stations. The basicidea is simply to allow other devices to relay thetraffic of a cellular user that receives weak or nosignal directly from its nearby cellular base sta-tions. This will clearly be useful to ensure thatthe dynamic shutting down of base stations, whilesaving energy, does not leave coverage holes.However, finding such relays can be a challenge.

COORDINATED MULTIPOINTTRANSMISSION AND RECEPTION

Another novel approach that can play a role inextending coverage is the use of coordinatedmultipoint transmission and reception (CoMP)being developed in the context of Long TermEvolution (LTE)-Advanced cellular networks [5].The basic idea in CoMP, a macro-diversityscheme, is to coordinate transmission and recep-tion at neighboring cells. As shown in Fig. 5,there are many users and five candidate base sta-tions. The dashed lines demonstrate the trans-mission ranges of the base stations. If intercellcooperation is not allowed, all five base stationsshould operate to guarantee the coveragerequirement (Fig. 5a). However, if the neighbor-ing base stations can transmit cooperatively (e.g.,BS2 and BS4, BS2 and BS3) to enlarge their cov-erage, all three users served by BS3 can be cov-ered, and BS3 can be switched off to save energy(Fig. 5b). Similarly on the uplink, coordinatedreceptions at multiple base stations can effective-ly reduce the received power requirement at eachindividual base station. The increased power onthe downlink and lowered power requirement atthe uplink together provide coverage with accept-able service quality for mobile users in nearbycells whose base stations have been shut downduring low activity periods [6].

HANDLING HETEROGENEOUS CELL SIZESIn our simplified estimate of energy savings, weconsidered uniform cell sizes. In third-/fourth-gen-eration (3G/4G) systems, because of the requiredhigh data rate, cellular towers will be more denseand have varying coverage, with more heteroge-neous cells, such as microcells, picocells, and fem-tocells. While most existing deployment strategiesfocus on quality of service during peak periods,energy savings during off-peak periods should also

be taken into account. In particular, heteroge-neous cellular architecture should be consideredin the deployment stage so that umbrella macro-cells can provide overall coverage with lower datarate, and smaller microcells/picocells can providebetter data rates for congested areas [7]. This canpotentially be exploited for energy efficiency, sincein the case of light load we can turn off (most of)the smaller cells. On the other hand, consideringenergy efficiency introduces additional complexityin network planning and deployment, and thusneeds to be further studied.

GRANULARITY OF CONTROLThe issue in dynamic base station switching is thetemporal granularity of the control. While most ofthe early work in this area has primarily focused onswitching off base stations once a day, it remains tobe seen whether finer-grained operation (e.g., onan hourly or even more frequent basis) can improveenergy efficiency, particularly in settings where traf-fic patterns are not predictable from day to day.However, this may require more active online mon-itoring of traffic and increase the complexity ofcoordination with other cells to ensure coverage.

Another related question is the spatial granu-larity of coordination. Base stations can beswitched off and on in a distributed manner ateither a base station controller or mobile switch-ing center level. This granularity can affect dif-ferent trade-offs between timeliness, complexityof coordination, and efficiency of the base sta-tion switching policy [8].

COOPERATION BETWEEN OPERATORSAs shown in our case study earlier, there isroom for greater energy savings if differentoperators can pool their resources. This is par-ticularly helpful in metropolitan areas whereeach operator sets up a dense deployment. Sucha scenario has also previously been examined byMarsan and Meo [9], who consider the settingof a single cell with two operators that cooper-ate with each other by accepting each others’traffic (as roaming traffic) when they are shutdown and show that considerable energy savingscan be obtained in such a setting. In implement-ing such cooperation, physical sharing may notbe a substantial concern. In many cases, inurban areas operator base stations tend to beclosely situated, or even on the same tower. Themain challenge is the complexity in networkoperation, regarding issues such as cross-opera-tor user authentication and billing, introducedby fine-granularity roaming.

This is also an interesting problem from agame theoretic perspective. Under what condi-tions would self-interested operators agree tocooperate with others? What kind of profit-sharing agreements will provide an adequateincentive for all participants? Cooperativegame theory formulations that consider coali-tion formation may provide insights on thesequestions. From a policy perspective, however,it also needs to be examined whether suchoperator agreements can potentially be abusedto create oligopolies that hurt customers, andhow these can be ameliorated in the interest ofgreen operation that offers other benefits tosociety.

Figure 5. Coverage extension with coordinated transmissions.

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MAINTAINING EMERGENCY LOCATION SERVICE

There are other practical considerations related tothe green operation of cellular infrastructure. Onesuch issue in the United States is E911 (Enhanced911). E911 is a North American telecommunica-tions-based system that automatically associatesthe physical address with a caller’s phone number,which enables the call to be routed to the mostappropriate public safety answering point for thataddress. In cellular networks, this requires locationdetermination of a cellular caller. In E911 PhaseII, it is required that 95 percent of a networkoperator’s in-service phones be E911 compliant,with a granularity of 300 m. Therefore, if cellulartowers are used to triangulate caller locations, the1-coverage requirement needs to be extended to3-coverage (i.e., each caller can reach three cellu-lar towers). However, as shown in a large-scalecellular network study by a major cellular operatorin the United States, over 50 percent of the time,a caller receives information from only one basestation instead of the three required for triangula-tion or trilateration [10]. Therefore, to achieve 3-coverage in the original cellular network, muchhigher cellular tower density is required. To main-tain 3-coverage at low traffic volume, we mightexpect a similar percentage of energy saving. Moredetailed analysis is needed. Note that there arealso alternative and complimentary techniquesthat address the issue of location determination,including using GPS data, combining with WiFilocation information, and using Bayesian infer-ence. Such location techniques apply in the case ofgreen cellular operation as well.

CONCLUDING COMMENTSIn this article we have discussed the currenttrends in green cellular technology. We havefocused on green cellular operation. Using realdata traces, we derived a first-order approxima-tion of the percentage of power saving one canexpect by turning off base stations during lowtraffic periods while maintaining coverage. Ourcoarse-grained analysis shows promising potential.We have also presented and discussed a numberof relevant challenges and solutions, such asmaintaining coverage, enabling cooperationbetween operators, and providing E911 service.

In summary, we argue that energy efficiency is animportant metric that should be used in the designand analysis of cellular networks, along with the met-rics that have been considered traditionally, such asblocking/dropping probability, throughput, and delay.While a few recent papers, such as [2, 6–9], havestarted to address this topic, there are still many richopen problems in this domain, and we encourage thecommunity to give it greater attention.

ACKNOWLEDGMENTSWe thank Thomas Lee, Yucho Ho, and SophiaTsang, undergraduate students at the University ofCalifornia, Davis, for their help in extracting thebase station information used in this article. Wethank Mr. Raj Savoor from AT&T Labs for a help-ful discussion regarding E911 and Mr. DonaldDraper Campbell from FCC for helpful inputregarding cellular coverage. The work of Dr. Ohwas supported by a National Research Foundation

of Korea Grant funded by the Korean Government(NRF-2009-352-D00205). The work of Dr. Niu ispartially supported by the National Science Fundfor Distinguished Young Scholars (60925002).

REFERENCES[1] G. Fettweis and E. Zimmermann, “ICT Energy Consump-

tion — Trends and Challenges,” Proc. IEEE WPMC, Lap-land, Finland, Sept. 8–11, 2008.

[2] M. A. Marsan et al., “Optimal Energy Savings in CellularAccess Networks,” Proc. IEEE ICC, Dresden, Germany,June 18, 2009.

[3] “Sustainable Energy Use in Mobile Communications,”White Paper, Ericsson, Aug. 2007.

[4] Ofcom, “Sitefinder: Mobile Phone Base StationDatabase,” http://www.sitefinder.ofcom.org.uk/.

[5] S. Parkvall and D. Astely, “The Evolution of LTE TowardsIMT-Advanced,” J. Commun., vol. 4, no. 3, Apr. 2009,pp. 146–54.

[6] D. Cao et al., “Energy Saving Performance Comparison ofCoordinated Multi-Point Transmission and Wireless Relay-ing,” Proc. IEEE GLOBECOM, Miami, FL, Dec. 6–10, 2010.

[7] A. J. Fehske, F. Richter, and G. P. Fettweis, “Energy Effi-ciency Improvements through Micro Sites in CellularMobile Radio Networks,” Proc. IEEE GLOBECOM, Hon-olulu, HI, Dec. 4, 2009.

[8] S. Zhou et al., “Green Mobile Access Network withDynamic Base Station Energy Saving,” Proc. ACM Mobi-Com (Poster Paper), Beijing, China, Sept. 20–25, 2009.

[9] M. A. Marsan and M. Meo, “Energy Efficient Manage-ment of two Cellular Access Networks,” Proc. ACM SIG-METRICS, Seattle, WA, June 15, 2009.

[10] Z. Hui, F. Baccelli, and J. Bolot, “Bayesian Inference forLocalization in Cellular Networks,” Proc. IEEE INFOCOM,San Diego, CA, Mar. 15–19, 2010.

BIOGRAPHIESEUNSUNG OH (eunsung.oh@usc.edu) received his B.S., M.S.,and Ph.D. degrees in electrical engineering from YonseiUniversity, Seoul, Korea, in 2003, 2006, and 2009, respec-tively. He is currently a post-doctoral researcher in theDepartment of Electrical Engineering at the University ofSouthern California’s Viterbi School of Engineering. Hismain research interests include the design and analysis ofalgorithms for green communication networks and next-generation wireless networks.

BHASKAR KRISHNAMACHARI (bkrishna@usc.edu) received hisB.E. in electrical engineering at The Cooper Union, NewYork, in 1998, and his M.S. and Ph.D. degrees from CornellUniversity in 1999 and 2002, respectively. He is currently anassociate professor and a Ming Hsieh Faculty Fellow in theDepartment of Electrical Engineering at the University ofSouthern California’s Viterbi School of Engineering. His pri-mary research interest is in the design and analysis of algo-rithms and protocols for next-generation wireless networks.

XIN LIU (liu@cs.ucdavis.edu) received her B.S. and M.S.degrees in electrical engineering from Xi’an Jiaotong Uni-versity, People’s Republic of China, in 1994 and 1997,respectively; and her Ph.D. degree from Purdue Universityin 2002. She is currently an associate professor in the Com-puter Science Department at the University of California,Davis. Before joining UC Davis, she was a postdoctoralresearch associate in the Coordinated Science Laboratory atUIUC. Her research interests include resource allocation andscheduling mechanisms in wireless networks, integration ofwireless and wireline networks, sensor networks, ultra-wideband communication, and timing channels.

ZHISHENG NIU [SM] (niuzhs@tsinghua.edu.cn) graduatedfrom Northern Jiaotong University, Beijing, China, in 1985,and got his M.E. and D.E. degrees from Toyohashi Universi-ty of Technology, Japan, in 1989 and 1992, respectively.After spending two years at Fujitsu Laboratories Ltd.,Kawasaki, Japan, he joined Tsinghua University, Beijing,China, in 1994, where he is now a full professor in theDepartment of Electronic Engineering. His current researchinterests include teletraffic theory, mobile Internet, radioresource management of wireless networks, and cognitiveand multiple radio networks. Currently, he is a Fellow andCouncilor of the IEICE, Director for Conference Publicationsof IEEE Communications Society, and council member ofthe Chinese Institute of Electronics (CIE).

We argue that ener-

gy efficiency is an

important metric

that should be used

in the design and

analysis of cellular

networks, along with

the metrics that have

been considered

traditionally, such as

blocking/dropping

probability,

throughput,

and delay.

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