IEEE Wireless Communications • February 201382 1536-1284/13/$25.00 © 2013 IEEE1 The static power con-sumption of a BS refe...
IEEE Wireless Communications • February 2013 83cooperation solutions, we investigate the chal-lenges for multicell coopera...
IEEE Wireless Communications • February 201384antee service requirements. Otherwise, it willresult in a high call blocking...
IEEE Wireless Communications • February 2013 85longer the distance, the greater the transmis-sion power required in order ...
IEEE Wireless Communications • February 201386tion, including the estimated amount of energyarrival and the amount of ener...
IEEE Wireless Communications • February 2013 87INCREASE ENERGY EFFICIENCY FORCELL EDGE COMMUNICATIONSBSs usually have lowe...
IEEE Wireless Communications • February 201388wireless channels, the scheduling delay involvedin the relaying process may ...
IEEE Wireless Communications • February 2013 89Incentive Mechanism — Note that in future hetero-geneous networks, multicel...
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  1. 1. IEEE Wireless Communications • February 201382 1536-1284/13/$25.00 © 2013 IEEE1 The static power con-sumption of a BS refers tothe power consumption ofthe BS when there are noactive users in the cover-age of the BS.MULTICELL COOPERATIONINTRODUCTIONGreening is not merely a trendy concept, but isbecoming a necessity to bolster social, environ-mental, and economic sustainability. Naturally,green communications has received much atten-tion recently. As cellular network infrastructuresand mobile devices proliferate, an increasingnumber of users rely on cellular networks intheir daily lives. As a result, the energy con-sumption of cellular networks keeps increasing.Therefore, greening cellular networks is attract-ing tremendous research efforts in bothacademia and industry [1]. Meanwhile, it hasbeen shown that, with the aid of multicell coop-eration, the performance of a cellular network interms of throughput and coverage can beenhanced significantly. However, the potential ofmulticell cooperation on improving the energyefficiency of cellular networks remains to beunlocked. This article overviews the multicellcooperation solutions for improving the energyefficiency of cellular networks.The energy consumption of a cellular net-work is mainly drawn from base stations (BSs),which account for more than 50 percent of theenergy consumption of the network. Thus,improving the energy efficiency of BSs is crucialto green cellular networks. Taking advantage ofmulticell cooperation, the energy efficiency ofcellular networks can be improved from threeaspects. The first one is to reduce the number ofactive BSs required to serve users in an area [2].The solutions are to adapt the network layoutaccording to traffic demands. The idea is toswitch off BSs when their traffic loads are belowa certain threshold for a certain period of time.When some BSs are switched off, radio coverageand service provisioning are taken care of bytheir neighboring cells. The second aspect is toassociate users with green BSs powered byrenewable energy. Through multicell coopera-tion, off-grid BSs enlarge their service area whileon-grid BSs shrink their service area. Zhou et al.[3] proposed the handover parameter tuningalgorithm and power control algorithm to guidemobile users to associate with BSs powered byrenewable energy, thus reducing on-grid powerexpenses. Han and Ansari [4] proposed an ener-gy-aware cell size adaptation algorithm namedICE. This algorithm balances the energy con-sumption among BSs, enables more users to beserved with green energy, and therefore reducesthe on-grid energy consumption. Envisioningfuture BSs to be powered by multiple types ofenergy sources (e.g., the grid, solar energy, andwind energy), Han and Ansari [5] proposed tooptimize the utilization of green energy for cel-lular networks by cell size optimization. The pro-posed algorithm achieves significant main gridenergy savings by scheduling the green energyconsumption along the time domain for individ-ual BSs, and balancing the green energy con-sumption among BSs for the cellular network.The third aspect is to exploit coordinatedmultipoint (CoMP) transmissions to improve theenergy efficiency of cellular networks [6]. Onone hand, with the aid of multicell cooperation,the energy efficiency of BSs on serving cell edgeusers is increased. On the other hand, the cover-age area of BSs can be expanded by adoptingmulticell cooperation, thus further reducing thenumber of active BSs required to cover a certainarea. In addition to discussing the multicellTAO HAN AND NIRWAN ANSARI, NEW JERSEY INSTITUTE OF TECHNOLOGYABSTRACTRecently, green communications has receivedmuch attention. Cellular networks are amongthe major energy hoggers of communication net-works, and their contributions to the globalenergy consumption increase fast. Therefore,greening cellular networks is crucial to reducingthe carbon footprint of information and commu-nications technology. In this article, we overviewthe multicell cooperation solutions for improvingthe energy efficiency of cellular networks. First,we introduce traffic-intensity-aware multicellcooperation, which adapts the network layout ofcellular networks according to user trafficdemands in order to reduce the number of activebase stations. Then we discuss energy-awaremulticell cooperation, which offloads traffic fromon-grid base stations to off-grid base stationspowered by renewable energy, thereby reducingthe on-grid power consumption. In addition, weinvestigate improving the energy efficiency ofcellular networks by exploiting coordinated mul-tipoint transmissions. Finally, we discuss thecharacteristics of future cellular networks, andthe challenges in achieving energy-efficient mul-ticell cooperation in future cellular networks.ON GREENING CELLULAR NETWORKS VIAMULTICELL COOPERATIONHAN LAYOUT_Layout 1 2/21/13 4:13 PM Page 82
  2. 2. IEEE Wireless Communications • February 2013 83cooperation solutions, we investigate the chal-lenges for multicell cooperation in future cellu-lar networks.TRAFFIC INTENSITY AWAREMULTICELL COOPERATIONThere are two reasons traffic demands of cellu-lar networks experience fluctuations. The firstone is the typical day-night behavior of users.Mobile users are usually more active in terms ofcell phone usage during the day than during thenight, and therefore, traffic demands during theday are higher than those at night. The secondreason is the mobility of users. Users tend tomove to their office districts during workinghours and come back to their residential areasafter work. This results in the need for largecapacity in both areas at peak usage hours, butreduced requirements during off-peak hours.However, cellular networks are usually dimen-sioned for peak hour traffic, and thus, most ofthe BSs work at low work load during off-peakhours. Due to the high static power consump-tion,1 BSs usually experience poor energy effi-ciency when they are operating under a lowwork load. In addition, cellular networks are typ-ically optimized for the purpose of providingcoverage rather than operating at full load.Therefore, even during peak hours, the utiliza-tion of BSs may be inefficient regarding energyusage. Adapting the network layout of cellularnetworks according to traffic demands has beenproposed to improve the energy efficiency of cel-lular networks. The network layout adaptation isachieved by dynamically switching BSs on/off.Figure 1 shows several scenarios of network lay-out adaptations. Figure 1a shows the originalnetwork layout, in which each BS has three sec-tors. For the green cell, if most of the trafficdemands on it are coming from sector three, andthe traffic demands in sectors one and two arelower than a predefined threshold, the green cellcould switch off sectors one and two to saveenergy, and the users in the sectors that areswitched off will be served by the neighboringcells. In this case, the network layout after theadaptation is shown in Fig. 1b. If traffic demandsfrom sector three of the green cell also decreasebelow the threshold, the entire green cell isswitched off, and the network layout is adapted,as shown in Fig. 1c. Under this scenario, theradio coverage and service provisioning in thegreen cell are taken care of by its active neigh-boring cells. When a BS is switched off, theenergy consumed by its radio transceivers, pro-cessing circuits, and air conditioners can besaved. Therefore, adapting the network layout ofcellular networks according to traffic demandscan save a significant amount of energy con-sumed by cellular networks.While network layout adaptation can poten-tially reduce the energy consumption of cellularnetworks, it must meet two service requirements:• The minimum coverage requirement• The minimum quality of service (QoS) require-ments of all mobile usersTherefore, in carrying out network layout adap-tation, multicell cooperation is needed to guar-Figure 1. Illustrations of network layout adaptations for macro cellular sites: a)original network layout; b) BS partially switched off; c) BS entirely switchedoff.(a)(b)(c)332133213321332133332133213321332133213321332133213321332133213321332133213321HAN LAYOUT_Layout 1 2/21/13 4:13 PM Page 83
  3. 3. IEEE Wireless Communications • February 201384antee service requirements. Otherwise, it willresult in a high call blocking probability andsevere QoS degradation. For example, two adja-cent BSs both experience low traffic demands.However, only one BS can be switched off tosave energy, and the other BS should be activeto sustain the service provisioning in both cover-age areas. In this case, if both BSs are switchedoff according to their own traffic demands, theirsubscribers will lose connections. Therefore,cooperation among BSs is essential to enabletraffic-intensity-aware network layout adapta-tion.COOPERATION TO ESTIMATE TRAFFIC DEMANDSNetwork layout adaptation is based on the esti-mated traffic demands at individual cells. Thetraffic demand estimation at individual cellsrequires the cooperation of their neighboringcells. To avoid frequently switching BSs on/off,they are switched off only when their trafficdemands are less than a predefined threshold fora minimum period, T. Therefore, the estimatedtraffic demands should represent the trafficdemands at individual cells for at least a timeperiod of length T. Hence, traffic demands at aBS consist of three parts: traffic demands fromusers who are currently attached to the BS, traf-fic demands from users who will hand over toother BSs, and traffic demands from users whowill hand over to the BS from its neighboringBSs. While the first two components can bemeasured and estimated by individual BSs, theestimation of the third component requirescooperation from neighboring cells. Two factorscontribute to the handover traffic from theneighboring cells. The first one is the user mobil-ity. A user’s motion, including the direction andvelocity, can be measured by various signal pro-cessing methods. Therefore, individual BSs canpredict:• How many users will hand over to other cellsin the near future• To which cells these users are highly likely tohand overSuch information is important for their neigh-boring BSs to estimate their traffic demands.The second reason for handover traffic is theswitching off of neighboring BSs. If one of theneighboring cells is switched off, the users underits coverage will be handed over to its neighbor-ing cells, thus increasing traffic demands of theneighboring cells. Therefore, cooperation amongradio cells is important for the traffic demandestimation at individual cells.COOPERATION TO OPTIMIZE THESWITCHING OFF STRATEGYWith the traffic demand estimation, cellularnetworks optimize the switching on/off strate-gies to maximize the energy saving while guar-anteeing users’ minimal service requirements.Currently, most existing switching on/off strate-gies are centralized algorithms, which assumethat there is a central controller that collectsthe operation information of all BSs and opti-mizes network layout adaptations. Three meth-ods have been proposed to determine whichBSs are to be switched off. The first one is ran-domly switching off BSs with low traffic loads.This method mainly applies to BSs in the nightzone, where few users are active. The methodrandomly switches off some BSs to save energy,and the remaining BSs provide coverage forthe area. The second method is a greedy algo-rithm, which enforces BSs with higher trafficloads to serve more users and switches off BSswith no traffic load. The third method is basedon the user–BS distance. The required trans-mission power of BSs for serving users dependson the distance between users and BSs. TheFigure 2. Energy utilization of the cellular network: a) on-grid energy consumption; b) renewable energy consumption.The renewable energy generation rate (kWh/h)(a)010Theon-gridenergyconsumption(kWh)2340.5x102x1021.0 1.5 2.0The renewable energy generation rate (kWh/h)(b)032Therenewableenergyconsumption(kWh)45670.5 1 1.5 2SSFCSO without sleepCSOSSFCSO without sleepCSOHAN LAYOUT_Layout 1 2/21/13 4:13 PM Page 84
  4. 4. IEEE Wireless Communications • February 2013 85longer the distance, the greater the transmis-sion power required in order to meet users’minimal service requirements. Therefore, theuser–BS distance can be an indicator of theenergy efficiency of cellular networks: theshorter the average user–BS distance, the high-er the energy efficiency. Hence, the algorithmtends to switch off BSs with the longestuser–BS distance to improve the energy effi-ciency of cellular networks.Centralized algorithms, however, require thechannel state information and traffic load infor-mation of every cell. Collecting this informationcentrally may impose tremendous communica-tion overheads, and thus reduce the effectivenessof centralized algorithms in improving energyefficiency. Therefore, distributed algorithms arefavored, especially for heterogeneous networksthat consist of various types of cells such asmacrocells, microcells, picocells, and femtocells.To enable distributed algorithms, individual cellsmay cooperatively form coalitions, and share thechannel state information and traffic load infor-mation. Based on the shared information, indi-vidual BSs optimize their operation strategies tominimize the total energy consumption of theBS coalition.ENERGY AWARE MULTICELL COOPERATIONWith the worldwide penetration of distributedelectricity generation at medium and low volt-ages, it is proved that renewable energy such assustainable biofuels, and solar and wind energycan be effectively utilized to substantiallyreduce carbon emission. Therefore, researchershave proposed to power BSs with renewableenergy to save the on-grid energy consumptionand reduce the carbon footprint. NokiaSiemens Networks [7] has developed off-gridBSs that rely on a combination of solar andwind power supported by fuel cell and deepcycle battery technologies. Compared to on-grid BSs, off-grid BSs achieve zero carbonemission and save a significant amount of on-grid energy. However, due to the dynamicnature of renewable resources, renewable ener-gy generation highly depends on environmentalfactors such as temperature, light intensity, andwind velocity. In addition, because of the limit-ed battery capacity, generated energy shouldbe utilized well to avoid energy overflow.Therefore, how to optimize the utilization ofrenewable energy in cellular networks is not atrivial problem. One intuitive solution for thisproblem is the energy-aware cell breathingmethod. Here, “energy-aware” pertains to twoperspectives. The first one is the awareness ofenergy sources such that users are guided todraw energy from off-grid BSs rather than on-grid BSs. This can be achieved by enlarging thecell sizes of off-grid BSs and shrinking those ofon-grid BSs [3]. The second perspective is theawareness of the amount of energy storagesuch that off-grid BSs with higher amounts ofenergy storage are forced to serve a largerarea. In this way, energy overflow can be avoid-ed, thus enabling the renewable energy genera-tion system to harvest more energy fromrenewable sources [4]. The energy-aware cellbreathing technique is a step forward to fur-ther traffic-intensity-aware network layoutadaptations. In addition to the traffic loadinformation and channel state information,energy-aware cell breathing requires multiplecells to cooperatively share the energy informa-Figure 3. Energy utilization of the cellular network: a) joint transmission; b) cooperative beamforming; c) cooperative relaying; d) dis-tributed space-time coding.Null steeringBeamforming(a)Base station 1 Base station 2User 1User 2(b)(c) (d)Base station 1 Base station 2Base stationUser 1 User 2Phase 1Phase 2Time slot 1Time slot 2Primary base stationSecondary user Relay nodeRelay nodeRelay nodeSecondary base stationPrimary userMobile usersHAN LAYOUT_Layout 1 2/21/13 4:13 PM Page 85
  5. 5. IEEE Wireless Communications • February 201386tion, including the estimated amount of energyarrival and the amount of energy storage, andto coordinate to minimize the energy consump-tion of cellular networks.A CASE STUDYEnvisioning future BSs powered by multipletypes of energy sources (e.g., on-grid energy andrenewable energy), we investigate the optimalenergy utilization strategies in cellular networkswith both on-grid and off-grid BSs. In such cel-lular networks, BSs are powered by renewableenergy if they have enough renewable energystorage; otherwise, the BSs switch to on-gridenergy to serve mobile users. To optimize ener-gy utilization, BSs cooperatively determine theircell size through energy-aware cell breathingbased on the proposed cell size optimization(CSO) algorithm [5]. By cooperative cell breath-ing, the on-grid energy consumption of the cel-lular network over a period of time consisting ofseveral cell size adaptations is minimized. Theoptimal energy utilization strategy includes twoparts: the multi-stage energy allocation policyand the single-stage energy consumption mini-mization. The multi-stage energy allocation poli-cy determines the amount of renewable energyallocated at individual off-grid BSs during eachcell size update. The single-stage energy con-sumption minimization involves two steps. Thefirst step is to minimize the number of activeon-grid BSs by offloading traffic cell breathing.On-grid BSs shrink their cell sizes to enforcethe associated users to hand over to off-gridBSs, and the off-grid BSs with higher amountsof energy storage enlarge their cell sizes toabsorb more users. The second step is to mini-mize the number of active BSs (both on-gridand off-grid) by applying the sleep mode check-ing algorithm. When traffic demands on individ-ual BSs are lower than a threshold, the BSs seekto put themselves into sleep mode by cooperat-ing with their neighboring cells. If the BS is insleep mode, its associated users will be handedover to its neighboring cells. In this case, if theneighboring BSs have enough energy storage toserve the handed over users, and the total ener-gy consumption of the cellular network isreduced, the BS is switched into sleep mode tosave energy.Figure 2 shows the comparisons between theCSO algorithm and the strongest-signal firstmethod, which always associates a user with theBS with the strongest received signal strength.Figure 2a shows the on-grid energy consumptionat different renewable energy generation rates.In this simulation, there are 400 mobile users inthe mobile network. As the renewable energygeneration rate increases, the on-grid energyconsumption of the mobile network is reduced.When the renewable energy generation rate islarger, more electricity is generated from renew-able energy. Therefore, more BSs can servemobile users using electricity generated byrenewable energy instead of consuming on-gridenergy. When the renewable energy generationrate is larger than 0.6 kWh/h, the CSO algorithmachieves zero on-grid energy consumption byoptimizing the cell size of each BS, while theSSF algorithm still consumes a significantamount of on-grid energy. When the renewableenergy generation rate is low, the CSO algo-rithm saves more than 200 kWh electricity thanthe SSF algorithm. As the generation rateincreases, the gap between the energy consump-tion of CSO and that of SSF narrows becausethe CSO algorithm has already achieved zeroenergy consumption, and the energy consump-tion of SSF reduces due to increased availabilityof renewable energy. Figure 2b shows the renew-able energy consumption at different energygeneration rates. When the energy generationrate is less than 0.7 kWh/h, the renewable energyconsumptions of all the simulated algorithmsincrease as the renewable energy generation rateincreases. This is because the larger the renew-able energy generation rate, the more renewableenergy can be used to serve mobile users. Inaddition, the CSO algorithm consumes morerenewable energy than the SSF algorithm doesbecause the CSO algorithm optimizes the cellsizes of BSs and maximizes the utilization ofrenewable energy in order to reduce the on-gridenergy consumption. When the renewable ener-gy generation rate is larger than 0.7 kWh/h, therenewable energy consumption of the CSO algo-rithm does not change much, while that of SSFkeeps increasing. This is because when therenewable energy generation rate is larger than0.7 kWh/h, the on-grid energy consumption ofthe CSO algorithm is zero, as shown in Fig. 2a.This indicates that all the users are served byrenewable energy. Therefore, the renewableenergy consumption of the CSO algorithmreflects the total energy consumption of the net-work, which is similar under different energygeneration rates in the simulation.ENERGY EFFICIENT COMP TRANSMISSIONCoordinated multipoint (CoMP) transmission isa key technology for future mobile communica-tion systems. In CoMP transmission, multipleBSs cooperatively transmit data to mobile usersto improve their receiving signal quality. BSsshare the required information for cooperationvia high-speed wired links. Based on the infor-mation, BSs determine joint processing andcoordinated scheduling strategies. While CoMPtransmission has been proved to be effective inimproving the data rate and spectral efficiencyof cell edge users [8], its potential for improvingthe energy efficiency of cellular networks has notbeen fully exploited. In this section, we discussthe potentials of CoMP transmission in greeningcellular networks.Figure 4. Cooperation to increase coverage area.Base station 1Cooperative coverage areaBase station 3Base station 2HAN LAYOUT_Layout 1 2/21/13 4:13 PM Page 86
  6. 6. IEEE Wireless Communications • February 2013 87INCREASE ENERGY EFFICIENCY FORCELL EDGE COMMUNICATIONSBSs usually have lower energy efficiency in serv-ing the users at the cell edge than in serving theusers within the cell. This is not only becausecell edge users are far away from BSs andrequire more transmission power, but alsobecause cell edge users experience more inter-ference from neighboring cells. Multicell cooper-ation can improve energy efficiency for cell edgecommunications [6].Joint Transmission — Joint transmission exploitsthe cooperation among neighboring BSs totransmit the same information to individualusers located at the cell edge [9]. Figure 3aillustrates the joint transmission scheme.User 2 associates with BS 2 while user 1 asso-ciates with BS 1. Instead of serving theirassociated users independently, BSs 1 and 2cooperatively transmit information to theirusers. For example, if both BSs apply time-division multiple access (TDMA) in the firsttime slot, both BS 1 and 2 transmit the sameinformation to user 2. The user combines thesignals from both BSs to decode the informa-tion. In the second time slot, the BSs cooper-atively serve user 1. Due to joint transmission,cell edge users experience higher receivingsignal strength and lower interference, hencerequiring less transmission power from bothBSs. Thus, joint transmission improves theenergy efficiency of the cell edge communica-tions.Cooperative Beamforming — In cooperative beam-forming, a BS forms radio beams to enhance thesignal strength of its serving users while formingnull steering toward users in its neighboringcells. As shown in Fig. 3b, BS 1 forms the radiobeam toward its associated user, user 1, toincrease the user’s receiving signal strength. Onthe other hand, in order to reduce the interfer-ence to user 2 who is served by a neighboringBS, BS 1 forms the null steering toward user 2.BS 2 executes the same operation as BS 1.Therefore, through cooperative beamforming,the signal-to-noise ratio (SNR) of both user 1and user 2 is enhanced. Thus, less transmissionpower is required for BSs to serve the cell edgeusers, and the energy efficiency of cell edgecommunications is increased.Cooperative Relaying — Taking advantage of cogni-tive radio techniques, cooperation can be exploit-ed between a primary cell and a secondary cellas shown in Fig. 3c. The secondary BS coopera-tively relays the signal from the primary BS tothe primary user. The primary user combines thesignals from both the primary and secondary BSsto decode the information. To stimulate cooper-ative relaying, the primary BS grants the sec-ondary BS some spectrum that can be utilizedfor communications between the secondary BSand secondary users. By cooperating with sec-ondary BSs, the transmission power requiredfrom primary BSs to serve the cell edge users isreduced. Therefore, the cooperative relayscheme reduces the power consumption of pri-mary BSs [10]. However, due to the time-varyingFigure 5. Future cellular networks with holistic cooperation.Coverage area of high-power green BSHigh-power BSLow-power BSSecondary BSUsersCoverage area of low-power BSCoverage area of secondary BSCoverage area of high-power on-grid BSTransmission beam of high-power BSTransmission beam of low-power BSTransmission beam of secondary BSCommunication link between BSsOwing to joint trans-mission, cell edgeusers experiencehigher receivingsignal strength andlower interference,therefore requiringless transmissionpower from bothBSs. Thus, the jointtransmissionimproves energyefficiency of thecell edgecommunications.HAN LAYOUT_Layout 1 2/21/13 4:13 PM Page 87
  7. 7. IEEE Wireless Communications • February 201388wireless channels, the scheduling delay involvedin the relaying process may increase the outageprobability. Fan et al. [11] showed that the out-age probability caused by the scheduling delaycan be alleviated by optimizing the transmissionpower of the network nodes.Distributed Space-Time Coding — Distributed space-time coding [12] explores the spatial diversityof both the relay nodes and the mobile usersto combat multipath fading, and can beapplied to improve the spectral and energyefficiency of cell edge users. As illustrated inFig. 3d, the distributed space-time codingscheme consists of two phases. In the firstphase, the BS broadcasts a data packet to itsdestination and the potential relays. In thesecond phase, the potential relays that candecode the data packet cooperatively transmitthe decoded data packet to the destinationusing a suitable space-time code. By exploringspatial diversity, distributed spaced-timecoded cooperative transmission improves thediversity gain of cellular networks. However,the multiplexing gain of the wireless systemmay be sacrificed [13]. Zou et al. [13] pro-posed an opportunistic distributed space-timecoding (O-DSTC) scheme to increase themultiplexing gain. Taking advantage of DSTCcooperative transmission, the spectral efficien-cy is increased. As a result, fewer transmis-sions are required in order to transmit a givennumber of data packets. Therefore, the ener-gy efficiency of the cellular networks isenhanced.ENABLE MORE BSS TO ENTER SLEEP MODEAs discussed in the previous subsection, CoMPtransmission (e.g., joint transmission) enhancesthe receiving SNR of cell edge users, implyingthat BSs are able to cover a larger area with thesame transmission power. Therefore, under lowtraffic demands, adopting the CoMP transmis-sion can reduce the number of BSs required tocover an area, thus improving the energy effi-ciency of cellular networks [14].As presented earlier, traffic-intensity-awaremulticell cooperation enables BSs with low traf-fic demands to go into sleep mode to save ener-gy. The users in the off cells will be served bytheir active neighboring cells. However, due tothe limitation of BSs’ maximal transmissionpower, a few BSs should stay awake to providecoverage in the area. By applying CoMP trans-mission, the number of required active BSs canbe further reduced. As illustrated in Fig. 4, theinner circle of BS 1 is the original coverage area,while the outer circle indicates the coverage areaafter cooperation with its neighboring BS, BS 3.The green area is the additional coverage areaachieved by multicell cooperation. Under thecondition of lower traffic demands, if there is nocooperation among BSs, three BSs have to stayawake to cover the whole area. However, if mul-ticell cooperation is enabled, BS 1 and BS 3 canprovide services to the users in the area by apply-ing cooperative transmission; thus, BS 2 can beswitched into sleep mode. Therefore, the totalenergy consumption of cellular networks isreduced.FUTURE CELLULAR NETWORKS WITHMULTICELL COOPERATIONWith advances of cellular networks and commu-nication techniques, future cellular networks willbe featured as heterogeneous networks in termsof both network deployments and communica-tion techniques, as shown in Fig. 5.Regarding network deployments, heteroge-neous networks refer to deploying a mix of high-power and low-power BSs in order to satisfy thetraffic demands of service areas. High-power BSs(e.g., macro and micro BSs) are deployed to pro-vide coverage to a large area, while low-powerBSs are deployed to provide high capacity withina small coverage area. In addition, based onenergy supplies, BSs can be further divided intotwo types: on-grid BSs, and off-grid BSs poweredby green energy such as solar power and windpower. Therefore, future heterogeneous net-works consist of four types of BSs: high-poweron-grid BSs, high-power off-grid BSs, low-poweron-grid BSs, and low-power off-grid BSs. Interms of technology diversity, heterogeneousnetworks consist of a variety of technologiessuch as multiple input multiple output (MIMO),cooperative networking, and cognitive network-ing. Taking advantage of the increasing networkdiversity, BSs have more cooperation opportuni-ties, and thus can achieve additional energy sav-ings. However, realizing optimal multicellcooperation is not trivial.Coalition Formulation — The problem of coalitionformation is to determine the coalition size andmembers of the coalition. Although the problemof coalition formation is well studied in the fieldof economics, and some game theory methodshave been applied to solve the coalition forma-tion problem in wireless networks, few modelscan be directly applied to form the coalitions ofBSs for the purpose of reducing the energy con-sumption of cellular networks. This is becausethe original coalition formation problem is toform a coalition to maximize the benefit of allthe members in the coalition, while the problemof forming coalitions of BSs is to maximize totalbenefits of the coalitions. In addition, due to thediversity of BSs and radio access technologies, asuccessful coalition may consist of BSs and tech-niques that are complementary to each other tomaximize the energy savings of the coalition.Hence, novel models or methods of coalitionforming must be developed to facilitate energy-efficient multicell cooperation.Green Energy Utilization — For the sake of environ-mental friendliness, off-grid BSs powered byrenewable energy are favored over on-grid BSs.In order to optimize the utilization of off-gridBSs, the fundamental design issue is to effective-ly utilize the harvested energy to sustain trafficdemands of users in the network. The optimalutilization of green energy over a period of timedepends on the characteristics of the energyarrival and energy consumption at the currentstage as well as in future stages, and on thecooperation strategies of the neighboring cells orcooperating cells.Taking advantage ofthe increasingnetwork diversity,BSs have morecooperationopportunities, andthus can achieveadditional energysavings. However,realizing optimalmulticell cooperationis not trivial.HAN LAYOUT_Layout 1 2/21/13 4:13 PM Page 88
  8. 8. IEEE Wireless Communications • February 2013 89Incentive Mechanism — Note that in future hetero-geneous networks, multicell cooperation mayinvolve cells not owned by the same operator.For example, the coalition may consist of a BSfrom a different operator, a secondary BS oper-ated via cognitive radio, and a relaying cellformed by mobile users. To incentivize coopera-tion among these cells, a simple but effectiveincentive mechanism should be exploited.CONCLUSIONThis article discusses how to reduce the energyconsumption of cellular networks via multicellcooperation. We focus on three multicell coop-eration scenarios that enhance energy efficiencyof cellular networks. The first one is traffic-intensity-aware multicell cooperation, in whichmultiple cells cooperatively estimate trafficdemands and adapt the network layout based onthe estimated traffic demands. Through networklayout adaptation, the number of active BSs canbe reduced, thus reducing the energy consump-tion of cellular networks. The second scenario isenergy-aware multicell cooperation, in which off-grid BSs powered by green energy are enforcedto serve a large area to reduce the on-grid powerconsumption. The third one is energy-efficientCoMP transmission, in which the overall energyconsumption is reduced by improving the energyefficiency of BSs in serving cell edge users. Inaddition, we discuss the diversity of future cellu-lar networks in terms of radio access technolo-gies and BS characteristics, and the challenges tooptimally exploit multicell cooperation by capi-talizing on diversities.REFERENCES[1] L. Correia et al., “Challenges and Enabling Technologiesfor Energy Aware Mobile Radio Networks,” IEEE Com-mun. Mag., vol. 48, no. 11, Nov. 2010, pp. 66–72.[2] Z. Niu, “Tango: Traffic-Aware Network Planning andGreen Operation,” IEEE Wireless Commun., vol. 18, no.5, Oct. 2011, pp. 25–29.[3] J. Zhou et al., “Energy Source Aware Target Cell Selectionand Coverage Optimization for Power Saving in CellularNetworks,” Proc. 2010 IEEE/ACM Int’l Conf. Green Com-puting and Commun. & Int’l Conf. Cyber, Physical andSocial Computing, Hangzhou, China, Dec. 2010.[4] T. Han and N. Ansari, “ICE: Intelligent Cell Breathing toOptimize the Utilization of Green Energy,” IEEE Com-mun. Letters, vol. 16, no. 6, June 2012, pp. 866–69.[5] T. Han and N. Ansari, “Optimizing Cell Size for EnergySaving in Cellular Networks with Hybrid Energy Sup-plies,” IEEE GLOBECOM ’12, Dec. 2012, pp. 3–7.[6] F. Heliot, M. Imran, and R. Tafazolli, “Energy EfficiencyAnalysis of Idealized Coordinated Multi-Point Commu-nication System,” IEEE VTC-Spring ’11, Budapest, Hun-gary, May 2011.[7] “E-Plus, Nokia Siemens Networks Build Germany’s FirstOff-Grid Base Station,”[8] R. 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Zou, Y.-D. Yao, and B. Zheng, “Opportunistic Dis-tributed Space-Time Coding for Decode-and-ForwardCooperation Systems,” IEEE Trans. Signal Proc., vol. 60,no. 4, Apr. 2012, pp. 1766–81.[14] M. Herlich and H. Karl, “Reducing Power Consumptionof Mobile Access Networks with Cooperation,” 2ndInt’l. Conf. Energy-Efficient Computing and Network-ing, New York City, NY, May 2011.BIOGRAPHIESTAO HAN [S’08] received his B.E. in electrical engineeringand M.E. in computer engineering from Dalian Universityof Technology and Beijing University of Posts and Telecom-munications, respectively. He is currently a Ph.D. candidatein the Department of Electrical and Computer Engineering,New Jersey Institute of Technology (NJIT), Newark. Hisresearch interests include mobile and cellular networks,network optimization, and energy-efficient networking.NIRWAN ANSARI [S’78, M’83, SM’94, F’09] received hisB.S.E.E. (summa cum laude with a perfect GPA) from NJIT,his M.S.E.E. from the University of Michigan, Ann Arbor,and his Ph.D. from Purdue University, West Lafayette, Indi-ana. He joined NJIT in 1988, where he is a professor of elec-trical and computer engineering. He has also assumedvarious administrative positions at NJIT. He has been a visit-ing (chair/honorary) professor at several universities. His cur-rent research focuses on various aspects of broadbandnetworks and multimedia communications. He has servedon the Editorial/Advisory Boards of eight journals. He waselected to serve on the IEEE Communications Society (Com-Soc) Board of Governors as a member-at-large (2013–2014)as well as IEEE Region 1 Board of Governors as the IEEENorth Jersey Section Chair. He has chaired ComSoc techni-cal committees, and has actively organized numerous IEEEinternational conferences/symposia/workshops, assumingleadership roles as Chair or TPC Chair for various confer-ences, symposia, and workshops. He has authored Compu-tational Intelligence for Optimization (Springer 1997) withE.S.H. Hou and Media Access Control and Resource Alloca-tion for Next Generation Passive Optical Networks (Springer,2013) with J. Zhang, and edited Neural Networks inTelecommunications (Springer 1994) with B. Yuhas. He hasalso contributed over 400 publications, over one third ofwhich were published in widely cited refereed journals/mag-azines. He has been granted over 15 U.S. patents. He hasalso guest edited a number of special issues, covering vari-ous emerging topics in communications and networking.He has frequently been selected to deliver keynote address-es, distinguished lectures, and tutorials. Some of his recentrecognitions include a couple of best paper awards, severalExcellence in Teaching Awards, a Thomas Alva EdisonPatent Award (2010), an NJ Inventors Hall of Fame Inventorof the Year Award (2012), and designation as a ComSocDistinguished Lecturer (2006–2009).The coalition mayconsist of a BS froma different operator,a secondary BS oper-ated via cognitiveradio, and a relayingcell formed bymobile users. Toincentivize coopera-tion among thesecells, a simple buteffective incentivemechanism shouldbe exploited.HAN LAYOUT_Layout 1 2/22/13 11:43 AM Page 89