IEEE Wireless Communications • October 2011 251536-1284/11/$25.00 © 2011 IEEE

INTRODUCTIONThe continuously growing demand for ubiquitousand broadband access to the Internet brings explo-sive development of the information and communi-cation technology (ICT) industry, which has becomeone of the major sources (responsible for 2–10 per-cent) of worldwide energy consumption and isexpected to increase further in the future. In themeantime, we have witnessed a consistent increasein the number of mobile terminals, especially in thecoming Internet of Things, which has triggeredmore complex and higher energy consuming signalprocessing technologies. Taking China as an exam-ple, it has already become the world’s largestmobile communication market with 860 millionmobile phones by the end of 2010, 300 million ofwhich are connected to the Internet. The totalenergy consumption of the three major Chineseoperators reached 289 billion kWh in 2009, a morethan 25 percent increase from 2008. Nevertheless,the penetration rate of mobile phones in China isonly 64 percent, which is still lower than the aver-age level of the developed countries. Thus, there isgreat potential for further growth of mobile trafficin the coming years; therefore, energy consumptionwill continuously be the major concern from boththe environmental and economic viewpoints.

Furthermore, the dominant traffic in wirelessnetworks has been shifting from mobile voice tomobile data, and further to mobile video in thefuture [1]. This transition is in fact one of the key

drivers of the evolution to new mobile broadbandstandards like third generation (3G), WiMAX,Long Term Evolution (LTE), and LTE-Advanced,resulting in the coexistence of macro-, micro-,pico-, and femtocells (i.e., heterogeneous cellularnetworks). The scarce spectrum resources have tobe divided into many independent pieces, andeach cellular network has to provide full coverageto its corresponding users by itself. Such a redun-dant deployment will definitely waste spectrum aswell as energy resources. In addition, with thedevelopment of mobile data and video, it is pre-dicted that the traffic volume of mobile servicesin 2015 could be as much as 100–1000 times whatthey are now, and two-thirds of that volume couldbe mobile video traffic [1]. Since the spectrum inwireless networks is limited in nature, this willlead to the widespread use of complex channelcoding and modulation techniques for advancedinterference mitigation. However, using suchtechniques typically implies accepting higherpower consumption not only from the transceiver,but also from the complete radio access network.As a result, the contribution of wireless networksto the global carbon footprint is forecast to dou-ble over the next 10 years.

According to the portfolio analysis of the totalenergy consumption in a typical mobile network,1it is reported that nearly 75 percent comes fromthe base station (BS) side; and inside a BS, near-ly 70 percent of the energy is consumed by poweramplifiers and air conditioners in order to keepthe BS working (i.e., providing coverage) eventhough there is no traffic in the cell. Therefore,only reducing transmitting power does not helphtotal energy savings too muc; the same is true ofincremental approaches such as slim BSs orsmart cooling technologies. A more ambitiousand system-wide solution is needed if some light-ly loaded BSs can be turned into sleep mode orcompletely switched off so that the correspond-ing power amplifiers and air conditioners canalso be shut down at those times.

In contrast, the existing wireless networks areusually dimensioned for performance optimizationwithout enough consideration of energy efficiency.Specifically, the so-called worst case network plan-ning philosophy has been widely adopted in orderto provide quality of service (QoS) guarantees

Deploying smaller butmore cells

ge Cell switched oflow traffic perio

IN V I T E D ART I C L E

ZHISHENG NIU, TSINGHUA UNIVERSITY

ABSTRACTThis article addresses the potential paradigm

shift of the next-generation cellular networks fromthe viewpoint of energy efficiency. In particular, itreveals that networks planning and operationshould be more energy efficiency oriented; and inthe meantime, the radio resources distributed overdifferent cellular networks, and base stationsshould be optimized in a global way to be globallyresource-optimized and energy-efficient networks(GREEN). A new framework, called traffic-awarenetwork planning and green operation (TANGO),is proposed toward GREEN. Some key technolo-gies for the migration to TANGO are then pre-sented and evaluated. Theoretical modeling andsimulation studies show that TANGO schemes cangreatly improve the energy efficiency of cellularnetworks, while keeping QoS at a satisfactory level.

TANGO: TRAFFIC-AWARENETWORK PLANNING AND GREEN OPERATION

This article addressesthe potentialparadigm shift of the next-generation cellular networksfrom the viewpointof energy efficiency.

1 https://www.ict-earth.eu/default.html

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even during peak traffic periods. As a result, dur-ing low traffic periods such as nights or holidays,or in some sparse spots where the traffic load tem-porarily gets very low due to user mobility, manyBSs are underutilized but still, by being active,consume a great amount of power. Consideringthe fact that the non-working time (including holi-days and nighttime) is in fact more than half ofthe year, the wasted energy of existing cellular net-works is remarkable. This is even more severe forfuture mobile communication networks where thesize of cells will be getting smaller and smaller(e.g., micro- or picocellular) in order to accommo-date more high-data-rate users and increase thefrequency reuse factor, which will further increasethe dynamics of the traffic in a specific cell. There-fore, it will be very important to have the transmit-ting power (and therefore energy consumed) ofnetwork nodes adapt to the traffic variation,including completely switching off some BSs whenthe traffic load is lower than a threshold. Howev-er, this will bring a grand challenge to us: how toguarantee cell coverage if the transmitting poweris reduced or a BS is switched off or turned intosleep mode. For example, how long should theBSs sleep?

In this article, we first claim that network plan-ning and operation should be more energy effi-ciency oriented; and, in the meantime, the radioresources distributed over heterogeneous cellularnetworks should be optimized in a global way, towhich we refer as globally resource-optimized andenergy-efficient networks (GREEN). Then wepropose a new framework called traffic-awarenetwork planning and green operation (TANGO)for GREEN, aimed at increasing the energy effi-ciency from the system point of view while guar-anteeing coverage and optimizing radio resourcesas well. Some key technologies for the migrationto TANGO are then presented and evaluated.Theoretical modeling and simulation studies showthat the TANGO schemes can greatly improvethe energy efficiency of cellular networks, whilethe QoS can be kept at a satisfactory level.

The article is organized as follows. The trafficdynamics and its opportunity are described.Based on that, several traffic-aware network

planning and green operation schemes are dis-cussed based on our previous studies. The con-clusions are then shown together with somefuture directions.

UNDERSTANDING THE TRAFFIC: TRAFFICDYNAMICS AND ITS OPPORTUNITY

As shown in Fig. 1, the traffic in a cellular net-work is typically unbalanced, changing not only inthe time domain but also in the spatial domain.Generally speaking, holiday or weekend traffic islower than weekday traffic, and night-time trafficis much lower than that during daytime. Duringdaytime, the so-called peak traffic period is onlya small portion of the whole day. On the otherhand, the traffic in different regions may also bevery different due to user mobility and the burstynature of data and video applications. For exam-ple, the business areas may be very heavily load-ed during the daytime but lightly loaded at night,which leads to unbalanced traffic load amongneighboring BSs even during the daytime. There-fore, if the capacity is planned based on the peaktraffic load for each cell, there will always besome cells under light load, while others areunder heavy load. In this case, any static celldeployment will not be optimal as traffic loadfluctuates. Such unbalanced traffic distributioncan be even more serious as the next-generationcellular networks move toward smaller cells suchas microcells, picocells, and femtocells.

Another trend of mobile traffic is that mobiledata and video will dominate whole networks[1]. On one hand, compared with voice traffic,data and video traffic are typically more burstyand dynamic, and therefore consume more spec-trum and energy resources [3]. On the otherhand, they can tolerate some delay in generaland, furthermore, are more point-to-multipoint(many people may be interested in the samecontent in a short time period) rather than justpoint-to-point communication. As a result, it willnot be energy-efficient to provide mobile dataand video services in a real-time point-to-pointway as we do voice traffic.

In summary, the traffic dynamics can in factprovide some opportunities for energy savings. Asshown in Fig. 2, if we can trace the traffic variationand adapt the radio resources (including transmit-ting power and other equipment’s power) in a cellor the whole cellular network to it, a great amountof energy could be saved. This just looks like agentleman (traffic needs) and a lady (power andother radio resources) dancing together in a har-monious way. In reality, more and more BSs havebeen equipped with self-organizing functionality oreven with sleep mode. For instance, Verizon hasstarted a Technical Purchasing Requirement(TPR) guideline by applying the Telecommunica-tion Equipment Energy Efficiency Rating(TEEER) methodology2 to all network compo-nents since 2009. Alternatively, if the network com-ponents cannot meet the TEEER criteria, they willnot be purchased no matter how cheap they areand how excellent their performance.

Figure 1. Traffic dynamics both in tthe ime and spatial domains [2].

Time (hour)240

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fic

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Center BSNeighbor BS 0Neighbor BS 1Neighbor BS 2Neighbor BS 3

2 http://www.verizonnebs.com/TPRs/VZ-TPR-9207.pdf

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IEEE Wireless Communications • October 2011 27

PARADIGM SHIFTS TO TANGO

FROM ALWAYS ON TO ALWAYS AVAILABLE WITHBS SLEEP CONTROL

As shown in Fig. 2, the traditional network plan-ning and operation is mainly based on theassumption that user requests may happen any-time and anyplace. This is in fact also the dreamthat people are expecting tof mobile communi-cations. As a result, the existing cellular net-works were mostly designed to keep thetransmitting power always on in order to guaran-tee cell coverage as well as provide the appropri-ate services if requests occurred. This is clearlynot energy-efficient because the user requestsoccur only sometimes and some places in prac-tice. It is therefore reasonable to keep the cellcoverage by a minimum number of BSs and thenadd necessary resources (additional channels orpower or even new BSs) on demand based onthe service requirements. In other words, thenetwork resources need only be always availablerather than always on if the coverage can beguaranteed. Apparently, this paradigm shift hasgreat potential for energy saving in cellular net-works. Of course, when some BSs are switchedoff or in sleep mode, radio coverage and serviceprovisioning are taken care of by the devicesthat remain active: BS cooperation is crucial.

BS sleeping is drawing more and more atten-tion in recent years. Reference [4] gives a staticBS sleep pattern according to a deterministic traf-fic variation pattern over time. However, neitherthe randomness nor the spatial variation of trafficis considered. Reference [5] proposes a resource-on-demand (RoD) strategy for high-density cen-tralized WLANs, where a cluster-head accesspoint (AP) takes care of the whole coverage inthe cluster so that other APs in the cluster can beswitched off when the traffic load is low. Howev-er, the channel model of WLANs is quite differ-ent from that of cellular networks where path lossis dominant. Therefore, a dynamic clusteringalgorithm considering BS collaboration is needed.

Our earlier work [6, 7] focuses on a scenariowhere traffic intensity varies in both time andspace domains. We have proposed an energy sav-ing algorithm that dynamically adjusts the workingmodes (active or sleeping) of BSs according to thetraffic variation with respect to a certain blockingprobability requirement. In addition, to preventfrequent mode switching, BSs are set to hold theircurrent working modes for at least a given inter-val. Simulations demonstrate that the proposedstrategy can greatly reduce energy consumptionwith blocking probability guaranteed, and the per-formance is insensitive to the mode holding timewithin a certain range. The problem is then mod-eled as a dynamic programming (DP) problem.Based on cooperation among neighboring BSs, alow-complexity algorithm is proposed to reducethe size of state space as well as that of actionspace. Figure 3 shows that with the proposed algo-rithm, the active BS pattern well meets the timevariation and non-uniform spatial distribution ofsystem traffic. Besides, the trade-off between theenergy saving from BS sleeping and the cost ofswitching is well balanced by the proposed scheme.

FROM STATIC TO DYNAMIC CELL PLANNING WITHCELL ZOOMING

Cell size in cellular networks is in general fixedbased on the estimated traffic load. However, asdiscussed earlier, the traffic load can have signif-icant spatial and temporal fluctuations, whichbring both challenges and opportunities to theplanning and operating of cellular networks. Inour early work [8], a dynamic cell planningscheme with cell zooming was proposed, wherethe cell size can be adaptively adjusted accordingto traffic conditions as well as the situation ofneighboring BSs in a collaborative way. Two typ-ical zooming patterns by increasing the transmit-ting power of those BSs that remain active andthe corresponding cell planning results areshown in Fig. 4. Unlike power control in the linklayer, which does not actually change the cellsize, cell zooming is a technique at the networklayer that changes the cell size by adjusting thetransmit power of control signals. Simulationresults show that both the centralized and dis-tributed algorithms can greatly reduce the ener-gy consumption of the whole network.

However, in practical networks, the zoomingcapability of some cells may be limited by the

Figure 2. Conceptual figure of the framework TANGO.

Time

Dynamics of mobile traffic

Power consumption of traditional cellular networks

Power consumption ofthe framework TANGO

Figure 3. Dynamic BS sleep control and its effect.

Time (h)50

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maximum transmitting power or other factors.As shown on the left of Fig. 5, if the zoomingcapability of the three center nodes is not suffi-cient to provide full coverage, all the neighbor-ing nodes surrounding these three nodes will notbe able to be switched off. This will definitelydeteriorate the energy saving performance of thecell zooming scheme. To solve this problem, wefurther proposed a new dynamic cell planningscheme in [9] by deploying more but smallercells. As shown on the right of Fig. 5, by adjust-ing the original cell size based on zooming capa-bility, more cells can be switched off comparedto the left side, and therefore more energy canbe saved. Although deploying more cells mayconsume a bit more power than the originalplan, the energy saving due to the switching offof more BSs is much higher in general. Thisresult is very important for energy-efficient cellplanning: the zooming capability should be takeninto account in the cell planning phase in orderto achieve more energy savings by cell zooming.

Another method to implement cell zooming isthe coverage extension techniques by coordinatedmultipoint (CoMP) transmission or wireless relays.In these cases, the zooming capability needs to beredefined, and the corresponding cell planningmechanism also needs to be established by consider-

ing the optimal relay placement [10] and the differ-ence in energy saving performance gain [11]. Asshown in Fig. 6 [11], depending on the traffic load,the energy saving gain can be divided into threeregions: coverage-limited, energy-efficient, and capaci-ty-limited. In the coverage-limited region where traf-fic load is relatively light, the energy saving gain isalways achievable but limited by coverage require-ment. On the contrary, almost no gain can beobtained in the capacity-limited region becauseincreasing the network capacity should be the firstpriority; therefore, no BSs or relay stations could beswitched off. The flexibility mainly comes from theenergy-efficient region, where the energy saving per-formance varies greatly according to the traffic load.

FROM UNIFORMED TO DIFFERENTIATED SERVICESWITH ENERGY-DELAY TRADE-OFF

As mentioned earlier, mobile data and video trafficwill dominate future networks. Unlike voice traffic,which is typically delay-sensitive and symmetric inuplink and downlink, data and video traffic are ingeneral loss-sensitive and asymmetric in uplink anddownlink. But the majority of existing cellular net-works were designed to accommodate voice trafficmainly; hence, if the capacity of the networks is notenough, they try to increase the capacity anyway(and therefore consume more power) or simplyreject requests (and therefore deteriorate QoS). Inother words, the existing cellular networks are notso friendly to data and video traffic. Since IP-baseddata and video traffic can tolerate some delay, theycan in fact be served in an opportunistic way.Specifically, they can be served during the periodwhen the channels are in good condition or the net-work is lightly loaded. For some data and videoapplications where many users are requesting con-tent within a short period, they can also be servedby multicast/broadcast [12], so they are not neces-sarily transmitted multiple times throughout thenetwork or are cached in between [13] so that userswho have the same request can get the servicelocally without going to the source node every time.This is the so-called soft-real-time service (i.e., usingdelay to trade for energy).

The question is then how much energy savingcan be traded off for a fixed amount of delay?The following example demonstrates that a shortdelay can in fact be traded off for a great amountof energy.

[Example] Consider two types of traffic: one isdelay-sensitive and the other is non-delay-sensitive.Both of them generate requests in Poisson withrate λi(i = 1, 2), and their service times are expo-nentially distributed with rate μi (i = 1, 2), respec-tively. The non-delay-sensitive traffic can toleratedelay kμ2

–1 maximum. Hence, they can be modeledas M/M/1(0) and M/M/1(k) queues, respectively.Based on the queueing theory, the blocking proba-bility PB

(i) of the two systems are given by

PB(1) = ρ1/(1 + ρ1), (1)

PB(2) = ρ2

k+1(1 – ρ2)/(1 – ρ2k+2). (2)

respectively, and the mean waiting time of thenon-delay-sensitive traffic is given byW2 = (1 + ρ2 + ··· + ρ2

k – kρ2k+2) μ2

–1/(1 – ρ2k+2)(3)

Figure 4. Possible switch-off patterns by cell zooming: a) (2, 3)-off scheme (2 out of 3 BSs are switched off; possible only for cells equipped with omnidi-rectional antennas, since cell direction changes after switch-off); b) (3, 4)-offscheme (3 out of 4 BSs are switched off. Possible for cells equipped with eitheromnidirectional or directional antennas, since cell direction does not change).

(a) (b)

Cell coverage in high traffic periodCell coverage in low traffic period

BS switched-off in low traffic period

BS

Figure 5. Solution for insufficient cell zooming in (2,3)-off scheme.

Coverage in hightraffic period

Deploying smaller butmore cellsTraditional planning

Maximum coverageby cell zooming

Cell switched off inlow traffic period

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where ρ1 = λ/ρ1 and ρ2 = λ/μ2. Suppose weneed to guarantee the blocking probability PBfor both traffic as the same. Then we get μ2 =μ1/s, where s = (1 – ρ2

k+1)/[(1 – ρ2)ρ2k]. That is,

with the average delay W2 or maximal delaykμ2

–1, the service rate can be reduced s times.Roughly speaking, if we consider the service isprovided by additive white Gaussian noise(AWGN) wireless channels, this means that thechannel capacity can be reduced s times; orequivalently, the transmitting power can besaved by 2s times based on Shannon’s formula.3For instance, taking k = 2 and ρ2 = 0.5, we get s= 7 and W2 = 1.7μ2

–1. If we increase k to 8 butρ2 = 0.5 remains unchanged, s = 511 and W2 =1.9μ2

–1! This clearly shows that a short delay canhelp save a great amount of energy.

Of course, the above-mentioned example isjust a conceptual illustration with many impracti-cal assumptions. More efforts are needed to losethese assumptions by considering, for example,non-Markovian arrival and service processes aswell as non-AWGN channels. The BS sleep con-trol policy and wireless relaying policy can alsobe jointly considered in order to evaluate thedelay-energy trade-off in a fairer and deeper way.

CONCLUSIONS

In this article, we have proposed a new frameworkcalled TANGO for traffic-aware dynamic cellplanning and green operation. Some key technolo-gies for the migration to TANGO, including:• From always on to always available with BS

sleep control• From static to dynamic cell planning with cell

zooming• From uniformed to differentiated services with

delay-energy trade-offhave been presented and evaluated. Theoreticalmodeling and simulation studies show that theTANGO schemes can greatly increase the ener-gy efficiency from the system point of view whileguaranteeing coverage and optimizing otherradio resources as well.

ACKNOWLEDGMENTThis work is sponsored in part by the NationalBasic Research Program of China(2012CB316001), the Nature Science Foundationof China (60925002, 61021001), and Hitachi Ltd.

REFERENCES[1] Cisco Visual Networking Index: “Global Mobile Data Traffic

Forecast Update 2010–2015,” http://www.cisco.com/[2] E. Oh et al., “Toward Dynamic Energy-Efficient Opera-

tion of Cellular Network Infrastructure,” IEEE Commun.Mag., vol. 49, no. 6, June 2011, pp. 56–61.

[3] D. Willkomm et al., “Primary User Behavior in CellularNetworks and Implications for Dynamic SpectrumAccess,” IEEE Commun. Mag., Mar. 2009.

[4] M. A. Marsan et al., “Optimal Energy Savings in CellularAccess Networks,” IEEE ICC ’09, Wksp. GreenComm,Dresden, Germany, June 2009.

[5] A.P. Jardosh et al., “Green WLANs: On-Demand WLAN

Infrastructures,” Mobile Networks and Apps., vol. 14,no. 6, Dec. 2009, pp. 798–814.

[6] S. Zhou et al., “Green Mobile Access Network withDynamic Base Station Energy Saving,” ACM MobiCom2009 (Poster), Beijing, China, Sept. 2009.

[7] J. Gong et al., “Traffic-Aware Base Station CooperativeSleeping in Dense Cellular Network,” IEEE IWQoS, Bei-jing, China, June 2010.

[8] Z. Niu et al., “Cell Zooming for Cost-Efficient Green Cel-lular Networks,” IEEE Commun. Mag., Nov. 2010.

[9] X. Weng, D. Cao, and Z. Niu, “Energy-Efficient CellularNetwork Planning under Insufficient Cell Zooming,”IEEE VTC-Spring ’11, Wksp. Greenet, Budapest, Hun-gary, May 2011.

[10] S. Zhou, A. Goldsmith, and Z. Niu, “On Optimal RelayPlacement and Sleep Control to Improve Energy Effi-ciency in Cellular Networks,” IEEE ICC ’11, Kyoto,Japan, May 2011.

[11] D. Cao et al., “Energy Saving Performance Comparisonof Coordinated Multi-Point Transmission and WirelessRelaying,” IEEE GLOBECOM ’10, Miami, FL, Dec. 2010.

[12] Z. Niu et al., “A New Paradigm for Mobile MultimediaBroadcasting based on Integrated Communication andBroadcast Networks,” IEEE Commun. Mag., vol. 46, no.7, July 2008, pp. 126–32.

[13] X. Wang, X. Liu, and Z. Niu, “On the Design of RelayCaching in Cellular Networks for Energy Efficiency,”IEEE INFOCOM ’11, Wksp. Green Communication andNetworks, Shanghai, China, Apr. 2011.

BIOGRAPHYZHISHENG NIU [M] (niuzhs@tsinghua.edu.cn) graduated fromNorthern Jiaotong University (currently Beijing JiaotongUniversity), China, in 1985, and got his M.E. and D.E.degrees from Toyohashi University of Technology, Japan, in1989 and 1992, respectively. After spending two years atFujitsu Laboratories Ltd., Kawasaki, Japan, he joinedTsinghua University, Beijing, China, in 1994, where he isnow a professor in the Department of Electronic Engineer-ing and deputy dean of the School of Information Scienceand Technology. He received Best Paper Awards from the13th and 15th Asia-Pacific Conference on Communication(APCC) in 2007 and 2009, respectively, and the Outstand-ing Young Researcher Award from the Natural ScienceFoundation of China in 2009. His current research interestsinclude teletraffic theory, mobile Internet, radio resourcemanagement of wireless networks, and green communica-tion and networks. Currently, He is a Fellow and Councilorof the IEICE, Director for Conference Publications of IEEECommunication Society, council member of the ChineseInstitute of Electronics (CIE), and Vice Chair of the Informa-tion and Communication Network Committee of the Chi-nese Institute of Communications (CIC).

Figure 6. Energy saving gain by BS cooperation and wireless relaying under dif-ferent traffic loads.

Traffic load (λA/(smaxμ))0.10

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Single BS transmissionBS cooperation (CoMP)Wireless relaying with β=5%Wireless relaying with β=10%

3 Precisely speaking, this is not always true because, underan AWGN channel, the log-based channel capacity for-mula tends to be a linear function of the transmit power atlow signal-to-noise ratio; therefore, the power will not fur-ther decrease exponentially when it reaches a certain smallvalue.

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