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Channel stateinformation

AC C E P T E D F R O M OPE N CA L L

INTRODUCTION

Information and communication technology(ICT) is playing a more and more important rolein global greenhouse gas emissions since theamount of energy for ICT is increasing dramati-cally with the explosive growth in service require-ments. It is reported that the total energyconsumed by the infrastructure of cellular wire-less networks, wired communication networks,and the Internet takes up more than 3 percentof the worldwide electric energy consumptionnowadays [1], and the portion is expected toincrease rapidly in the future. As an importantpart of ICT, wireless communications areresponsible for energy saving. On the otherhand, mobile terminals in wireless systems neces-sitate energy saving since the development of battery technology is much slower than theincrease of energy consumption. Therefore, pur-suing high energy efficiency (EE) is a trend forthe design of future wireless communications.

During the past decades, much effort hasbeen made to enhance network throughput. Dif-ferent network deployments have been wellinvestigated to improve area spectral efficiency(ASE), such as optimization of the number of base stations (BSs) in cellular networks and theplacement of relay nodes in relay systems.

Numerous resource allocation schemes have

been proposed to ensure the quality of service(QoS) of each user and fairness among differentusers by exploiting multi-user diversity. Manyadvanced communication techniques, such as

orthogonal frequency-division multiple access(OFDMA), multiple-input multiple-output(MIMO) techniques, and relay transmission,have been fully exploited in wireless networks toprovide high spectral efficiency (SE). However,high network throughput usually implies largeenergy consumption, which is sometimes unaf-fordable for energy-aware networks or energy-limited devices. Figuring out how to reduceenergy consumption while meeting throughputrequirements in such networks and devices is anurgent task.

Recently, energy-efficient system design hasreceived much attention in both industriy andacademia. In the industrial area, both vendors

and operators are expecting more energy-savingdevices to reduce manufacturing or operatingcost. Several projects and organizations, such asEnergy Aware Radio and Network Technologies(EARTH), have been set up to develop moreenergy-efficient architectures and techniques.On the other hand, some valuable papers havebeen published, and workshops on green radiohave been organized at many international con-ferences, such as ICC and GLOBECOM. Vari-ous energy-efficient methods have beenproposed for different layers of wireless networks. For network planning, the impact of cellsizes on EE in cellular networks has been stud-ied [2]. It has been shown that reducing cell sizecan increase the number of delivered informa-tion bits per unit energy for given user densityand total power in the service area. If a sleepmode is introduced, the EE can be furtherenhanced. In addition, mixed cell deployment(e.g., using microcells at the edge of a macro-cell), is also an efficient way to save energy as well as to enhance the performance of cell edgeusers. For the medium access control (MAC)layer, protocols have been designed to efficientlyutilize resources (e.g., power, time slots, and fre-quency bands) to reduce energy consumption.For the physical layer, different transmissiontechniques have been reconsidered from the EE

point of view instead of traditional SE. Some

GEOFFREY YE LI, ZHIKUN XU, CONG XIONG, CHENYANG YANG, SHUNQING ZHANG,

YAN CHEN, AND SHUGONG XU

ABSTRACT

With explosive growth of high-data-rate appli-cations, more and more energy is consumed in

wireless networks to guarantee quality of service.Therefore, energy-efficient communications havebeen paid increasing attention under the back-ground of limited energy resource and environ-mental-friendly transmission behaviors. In thisarticle, basic concepts of energy-efficient com-munications are first introduced and then exist-ing fundamental works and advanced techniquesfor energy efficiency are summarized, includinginformation-theoretic analysis, OFDMA networks, MIMO techniques, relay transmission,and resource allocation for signaling. Some valu-able topics in energy-efficient design are alsoidentified for future research.

ENERGY-EFFICIENT WIRELESS COMMUNICATIONS:

TUTORIAL, SURVEY, AND OPEN ISSUES

The authors

introduce basic

concepts ofenergy-efficient

communications,

and summarize

existing fundamental

works and advanced

techniques for

energy efficiency.


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cross-layer approaches have also been developedto obtain more gain over the independent layerdesign [3].

In this article, we mainly focus on techniquesin physical and MAC layers. Cross-layer EE opti-mization in time, frequency, and spatial domains was discussed in [3] while four fundamental trade-offs, including deployment efficiency–EE, spectralefficiency–EE, bandwidth–power, and delay–power, were studied in [4]. Different from them, we dis-cuss these topics from the perspective of how to

develop specific energy-efficient techniques.Specifically, fundamentals of energy-efficientcommunications are first introduced, includingthe information-theoretic bounds and the impactof some practical issues. Multiple access tech-niques considering EE are discussed, where thedesign of energy-efficient OFDMA systems isemphasized since a comprehensive survey on EEin code-division multiple access (CDMA) networks was presented in [5]. Next, some advancedtechniques, including MIMO and relay, are elab-orated. Although these techniques can improveSE significantly, it comes at significant cost,including additional configuration of antennas or

relay stations and additional energy consump-tion. How to design energy-efficient MIMO andrelay systems is covered, respectively. We discusssignaling design considering EE and focus onthe resource allocation between signaling anddata symbols. We then conclude the article.

FUNDAMENTALS

SE is a widely used performance indicator forthe design of wireless communication systems.SE-oriented systems are designed to maximizeSE under peak or average power constraints, which may lead to transmitting with the maxi-mum allowed power for a long period and thus

deviate from energy-efficient design.During the past decades, EE, which is com-

monly defined as information bits per unit of transmit energy, has been studied from the infor-mation-theoretic perspective for various scenar-ios [6]. For an additive white Gaussian noise(AWGN) channel, it is well known that for agiven transmit power, P, and system bandwidth, B, the channel capacity is

bits per real dimension or degrees of freedom(DOF) [7, Ch. 5], where N 0 is the noise powerspectral density. According to the Nyquist sam-pling theory, DOF per second is 2 B. Therefore,the channel capacity is C = 2 BR b/s. Conse-quently, EE is [4, 8]

(1)

From Eq. 1, it is obvious that η EE decreasesmonotonically with R, with (η EE) max = 1/( N 0ln2)as R → 0, and (η EE) min = 0 as R → ∞.

The result in Eq. 1 is obtained by assumingan infinite size of information block and infinitenumber of DOF. However, the system behavior

is totally different in the finite case. It is shown

in [8] that noiseless feedback leads to much bet-ter EE in this case, while availability of noiselessfeedback does not improve EE in the infinitecase. Moreover, bounds on EE for the finitecase have been derived in [9] for a given trans-mission rate. Results on EE in the widebandregime for many other types of channels can befound in [6].

The EE bounds derived from the informa-tion-theoretic analysis might not be achieved inpractical systems due to performance loss of capacity-approaching channel codes, imperfectknowledge of channel state information (CSI)[10], cost of synchronization [11], and transmis-

sion associated electronic circuit energy con-sumption [12–16]. Among these factors,electronic circuit energy consumption changesthe fundamental trade-off between EE and datarate. Taking circuit energy consumption intoconsideration, EE needs to be redefined asinformation bits per unit of energy (not onlytransmit energy), where an additional circuitpower factor, P c, needs to be added in thedenominator of Eq. 1. Accordingly, the η EE vs. Rcurve will turn from a cup shape to a bell shape,as shown in Fig. 1 from [4]. It is obvious that EE will decrease with the circuit power. As a result,circuit consumption may change our view of con- ventional energy saving techniques like MIMO[13], discussed later. To analyze the impact of circuit power on EE quantitatively, detailedmodeling of equipment-level energy consump-tion of devices such as base stations (BSs) andmobile terminals is very helpful. Circuit power isusually modeled as a constant, which is indepen-dent of data transmission rate [12, 15]. Recently,it has been found that it is more accurate some-times to model it as a linear function of datarate [16]. In [12], a detailed circuit model hasbeen established for a 2.5 GHz radio band ener-gy-limited transceiver. From there, it can be seenthat the circuit energy consumption of a trans-mitter adds up to 50 mW, while the peak trans-

mit power is 250 mW. As shown in [17], the

η EE R

C

P

R

N

= =

−

2

2 102( )

.

RP

N B= +

⎛

⎝⎜⎞

⎠⎟1

212

0

log

Figure 1. Trade-off between EE (ηEE ) and R in an AWGN channel.

R (b/DOF)

Pc(1) < P

c(2) < P

c(3)

1/ N0In2

0

η E E

( b / J )

Without circuit consumption

With circuit consumption Pc(1)




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power consumption of a commercial 802.11gtransceiver consumes 990 mW at the idle modeand 1980 mW at the transmit mode. These twoexamples also corroborate that the circuit energy

consumption is not always negligible comparedto the transmit power.

OFDMA NETWORKS

OFDMA has been extensively studied for next-generation wireless communication systems, suchas Worldwide Interoperability for Microwave Access (WiMAX) and the Third GenerationPartnership Project (3GPP) Long Term Evolu-tion (LTE). In OFDMA, system resource, suchas subcarriers and transmit power, needs to beproperly allocated to different users to achievehigh performance. Figure 2 illustrates theresource allocation of a downlink OFDMA net-

work, where subcarriers and power are allocatedbased on users’ CSI and QoS requirements bythe BS. The two most commonly used classes of dynamic resource allocation schemes are rateadaptation (RA), which maximizes throughput,and margin adaptation (MA), which minimizestotal transmit power [18]. Therefore, RA aims atSE, while MA targets on transmit power effi-ciency. However, neither of them is necessarilyenergy-efficient. While OFDMA can providehigh throughput and SE, its energy consumptionis sometimes large. In this section, we focus onenergy-efficient resource allocation schemes forOFDMA systems.

Energy-efficient orthogonal frequency-divi-sion multiplexing (OFDM) systems, a specialcase of OFDMA, have been first addressed withconsideration of circuit consumption for fre-quency-selective fading channels [14]. In contrastto the traditional spectral-efficient water-fillingscheme that maximizes throughput under a fixedoverall transmit power constraint, the newscheme maximizes the overall EE by adjustingboth the total transmit power and its distributionamong subcarriers. It is demonstrated that thereis at least a 15 percent reduction in energy con-sumption when frequency diversity is exploited.

Energy-efficient design has also been extend-ed to general OFDMA networks [19]. For uplink

transmission with flat fading channels, it is

shown that using adaptive modulation, the EEincreases as the user moves toward the BS, andthe closer the user is to the BS, the higher themodulation order should be.

In an interference-free environment, a trade-off between EE and SE exists, for increasingtransmit power always improves SE but withoutguarantee of EE improvement. However, inmulticell interference-limited scenarios, increas-ing transmit power even does not necessarilybenefit SE due to the associated higher interfer-ence to the network. In [20], energy-efficientdesign in multicell scenarios with intercell inter-ference is studied. As shown there, energy-effi-cient power distribution not only boosts systemEE but also refines the EE-SE trade-off due tothe conservative nature of power allocation, which sufficiently restricts interference fromother cells and improves network throughput.

The existing research on energy-efficientOFDMA has mainly focused on uplink scenar-ios or mobile terminal sides. More effort shouldbe put on the downlink or BS sides for the greendesign target. In addition, the impact of knowl-edge of traffic statistics has not been investigat-ed. Moreover, the general EE-SE trade-off isnot addressed yet. Further research on the fol-lowing aspects is desired.

Energy-efficient transmission in the down-link: In many situations, downlink EE is also very important. For example, it might be desiredthat the construction of BSs in cellular networkshave environment-friendly behavior and lessexpenditure for energy consumption. Also, thedownlink OFDMA energy-efficient communica-tion is different from the uplink; subcarrier allo-cation, power allocation, and rate adaption needto be jointly addressed. Thus, it may n ot bedirectly extended from the uplink case.

The role of traffic statistics: It is crucial inenergy-efficient broadband communications.Existing approaches should be modified to incor-porate traffic statistics, which may be acquiredfrom queue status of each user. Depending onthe traffic, the lengths of the active and sleepperiods can be dynamically assigned, and thepower, modulation order, and coding can beadjusted jointly to achieve desirable EE.

Trade-off between EE and SE: Since EE

Figure 2. Resource allocation in OFDMA [3].

Time

OFDMA

Queue stateinformation

Channel stateinformation

File downloading

Subcarrier andpower allocation

Radio resource

F r e q u e n c y

Online gaming

Video streaming

In contrast to the

traditional spectral-

efficient water-filling

scheme that maxi-

mizes throughput

under a fixed overall

transmit power con-straint, the new

scheme maximizes

the overall EE by

adjusting both the

total transmit power

and its distribution

among subcarriers.



and SE are two important system performanceindicators, the trade-off between EE and SEfor general OFDMA networks should beexploited to guide system design. The boundsand achievable EE-SE regions for downlinkOFDMA networks are important for the sys-tem designer. Meanwhile, proper utility func-tion should be investigated for locating the

optimum operating point on the boundary of EE-SE region.

MIMO TECHNIQUES

MIMO techniques have been widely adopted in wireless networks nowadays. As shown in Fig. 3,single-input single-output (SISO), single-inputmultiple-output (SIMO), and multiple-input sin-gle-output (MISO) can be regarded as specialcases of MIMO. MIMO can also be used withsingle users or multiple users to form single-userMIMO (SU-MIMO), multi-user MIMO (MU-MIMO), and coordinated multipoint (CoMP)transmission. It has been demonstrated in thesespecifications that spatial DOF from configura-tion of multiple antennas enhances both reliabil-ity and capacity. For example, in the downlink of 3GPP LTE, both SU-MIMO and MU-MIMOmodes are supported, and different modes canbe selected according to the specific require-ment. In 3GPP LTE-Advanced, CoMP tech-niques have been proposed to further improvethe throughput of cell edge users and the cover-age.

Although MIMO techniques have been shownto be effective in improving capacity and SE of wireless systems, energy consumption alsoincreases. First of all, more circuit energy is con-

sumed due to the duplication of transmit or

receive antennas. Depending on the ratio of theextra capacity improvement and the extra energyconsumption, the EE of a multiple-antenna sys-tem may be lower than that of a single-antennasystem. Moreover, more time or frequencyresources are spent on the signaling overheadfor MIMO transmission. For example, in most of MIMO schemes, CSI is required at the receiver

or at both the transmitter and the receiver toobtain good performance. In order to estimatethe CSI and feed it back to the transmitter,some training symbols need to be sent beforethe data transmission. Since the number of chan-nel coefficients increases with the product of thenumber of transmit antennas and that of receiveantennas, much more signaling overhead isrequired for MIMO systems. The EE of MIMOsystems is still unknown if all the overhead isconsidered.

Some preliminary results on this topic havebeen presented in the literature. Adaptivelychanging the number of active antennas at theBS is proposed for 3GPP LTE to address thelarge traffic variation issue in cellular networks[21]. According to statistics, the number of active users at night is much lower than that inthe day. Switching off some radio frequency(RF) amplifier units at night can save energy sig-nificantly while maintaining QoS of active users.In [22], adaptive switching between MIMO andSIMO is addressed to save energy at mobile ter-minals. The characteristic of dynamic user popu-lation is well exploited for joint MIMO modeswitching and rate selection. The EE of Alam-outi diversity schemes has been discussed in [13].It is shown that for short-range transmission,MISO decreases EE compared with single-anten-

na transmission if they are not combined with

Figure 3. Diagram of MIMO schemes.

ChannelTx Rx SU-MIMOSISO

MU-MIMO

CoMP

ChannelTx RxSIMO

ChannelTx RxMIMO

ChannelTx RxMISO

Since the number of

channel coefficients

increases with the

product of the num-

ber of transmit

antennas and that of

receive antennas,much more signaling

overhead is required

for MIMO systems.

The EE of MIMO sys-

tems is still unknown

if all the overhead is

considered.



adaptive modulation. However, by adaptingmodulation order to balance transmit energyand circuit energy consumption, MISO systemsoutperform SISO systems. Different MIMOschemes may have different EE in different sce-narios. In [23], adaptive switching strategy amongdifferent MIMO modes is investigated. Space-division multiplexing, space-time coding, andSISO transmission are adapted based on theCSI. It is shown that smart adaptation canachieve a better EE-SE trade-off than single-MIMO mode and improvement of EE up to 30percent compared to non-adaptive systems.Existing research on the EE of MIMO tech-

niques mainly focuses on open-loop SU-MIMOschemes. A lot of potential research can bedeveloped in other aspects of MIMO schemes tofurther improve EE. Some possible topics are asfollows.

Closed-loop MIMO schemes: Closed-loopMIMO schemes, such as beamforming and pre-coding, are shown to enhance SE efficiently.However, the overhead for CSI feedback willconsume additional radio resources, includingtime, bandwidth, and power. Whether or whenclosed-loop MIMO schemes are more helpfulthan open-loop ones to save energy is still anopen issue.

Energy-efficient MIMO schemes in multi-

user and multicell scenarios: In multi-user andmulticell environments, the existence of interus-er and intercell interference complicates thedesign of energy-efficient MIMO systems. Howto utilize the spatial resource to maximize EE while suppressing interference is well worthinvestigating.

Energy-efficient MIMO-OFDMA systems:MIMO schemes are usually incorporated intoOFDMA systems. The spatial and frequencyresource can be jointly allocated to improve EE.However, the complexity of the joint design maybe prohibitive. Effective but simple algorithmsneed to be developed to obtain a trade-off

between complexity and performance.

RELAY TRANSMISSION

Relay in wireless networks provides another wayto improve performance and potentially saveenergy. By deploying relay nodes, more connec-tions between the source node and the destina-tion node are built, and data from the sourcenode can be delivered through multiple wirelesslinks. Due to independence among different fad-ing channels/links, diversity gain can be obtained,and SE can be consequently improved. There-

fore, the time to transmit a fixed amount of datais reduced, and so is the consumed energy. If advanced resource allocation schemes areapplied, energy can be further saved.

In a typical relay system, a transmission peri-od consists of two phases: broadcasting and mul-tiple access. During the broadcasting phase, thesource node sends data over the air, which maybe received by the relay nodes, or both the relayand destination nodes. During the multi-accessphase, the relay nodes, or both the source andrelay nodes transmit data to the destinationnodes. Note that the nodes to transmit andreceive in these two phases depend on the spe-

cific protocols. The transmission schemes at therelay nodes can be amplify-and-forward (AF) ordetect-and-forward (DF) transmission methods.

As shown in Fig. 4, two kinds of relay systemsare considered in the literature: pure relay sys-tems and cooperative relay systems [24]. For thepure relay systems, the role of the relay nodes isonly to help the source node to transmit data, while in the cooperative relay systems, all thenodes act as information sources as well as relays.

PURE RELAY SYSTEMS

For pure relay systems, a critical problem is howto use the relay nodes efficiently, including howmany relay nodes are needed for data delivery

and how the relay nodes are configured. TheEE-SE trade-off of pure relay systems in AWGNrelay channels has been investigated in [25], where the optimal power allocation among relaynodes is proposed to maximize EE. It has beenshown that the performance (either consumedenergy or data rate) depends on the transmissionstrategy of each node, the locations of the relaynodes, and the data rate used by each node. Twosuboptimal communication schemes, commonrate and common power schemes, are proposedto capture the inherent constraints of networks,bandwidth, and energy. Figure 5, from [25],demonstrates the impact of the hop number,node locations, and data rate on EE. Althoughpower allocation, and the number and locationsof nodes affect the EE significantly, such jointdesign is very complex and may not be suitablefor some practical scenarios. Some simple andeffective relay transmission strategies have beenproposed. In order to simplify the relay network,only two-hop communications are set upbetween the source and destination nodes. Dif-ferent relay selection schemes have been pro-posed in [26, 27]. In [26], the best relay node isselected distributively, while in [27], several relaynodes are selected for beamforming based on asimple selection strategy. It is shown that the EEmay not increase with the number of relay nodes

due to cooperation overhead.

Figure 4. Two structures of relay systems.

Base station Relay station User

(a) (b)



COOPERATIVE RELAY SYSTEMS

Different from the case of pure relay systems,cooperation among users makes it more complexto optimize resource management. The first dif-ficulty is that resources at each user should besplit for transmitting data both from itself andfrom other users, besides allocating resourcesamong different users. The second one stemsfrom partner selection, finding an appropriateuser as a relay node. It is very complicated to

find the optimal partner in a network with alarge number of users since the number of possi-ble pairings is huge.

Cooperative relay systems have been widelystudied. In [28], a network with two users is con-sidered, and power is optimally allocated to max-imize the EE of each user in a distributed way.It is shown that user cooperation can improveusers’ EE. In [29], power minimization problemis formulated with constraints on each user’sdata rate. Cooperative user pairing, power allo-cation, and subcarrier mapping are jointly opti-mized. In [30], the EE of cooperative access witha relay’s data protocol is analyzed for multirate

wireless local area networks (WLANs), consider-ing transmission errors.

POTENTIAL RESEARCH TOPICS

Existing research results have shown that relaysystems can improve EE significantly. However,several important issues are still open.

Relay transmission considering the overhead: Additional time and power may be used forresource allocation during relay transmission.How to minimize the total energy consumptiontaking the additional overhead into account isnot known clearly.

Energy-efficient bidirectional relay systems:Bidirectional relaying is a booming technique

and provides more opportunity to save energy.How to design energy-efficient bidirectionalrelaying systems is an interesting topic.

Relay transmission in multicell environ-ments: Most existing work focuses on single-point-to-single-point transmission; how toallocate resources in multipoint-to-single-pointor multipoint-to-multipoint transmission, as inthe multicell case, still needs further investiga-tion.

RESOURCE ALLOCATION BETWEEN

SIGNALING AND DATA SYMBOLS

Besides data streams, signaling symbols are widely used to assist data transmission in wire-less communications. Representative are sig-naling for synchronization and channelestimation. In the beginning, resource alloca-tion for signaling symbols is independent of that for data symbols. For example, the num-ber and power of training sequences for chan-nel estimation is only determined by therequired estimation accuracy. However, theseparation of signaling and data symbol designsdoes not optimize system performance. There-fore, joint resource allocation between signal-ing and data symbols is very important for

energy-efficient design.

Asynchronous EE is investigated in [11] forscenarios in which the cost of acquiring syn-chronization is significant. It is shown that theextent of EE reduction in the asynchronouscase, unlike in the synchronous case, dependson the measure of timing uncertainty. EE con-sidering training-based channel estimation isstudied in [10]. Through Gaussian assumptionof interference incurred by channel estimationerror, it is demonstrated that EE decreases tozero as the SNR goes to zero, and the maxi-mum EE is achieved at a nonzero SNR value,

as shown in Fig. 6. The figure also implies thatthe relationship between EE and SE is nolonger a monotonically decreasing function.The EE of training-based schemes is also inves-tigated in [10] when the channel input vectorin each coherence block is subject to a peakpower constraint. Optimal resource allocationto maximize EE is obtained through numericalanalysis.

In general, study of resource allocationbetween signaling and data symbols is only inthe initial stage. A lot of open issues need to beinvestigated, as listed below.

Resource allocation between signaling anddata symbols in multi-user cases: The EEstudy in the existing literature is limited to thepoint-to-point case. In the multi-user case, dif-ferent users may suffer from different channelfading, which results in different requirementsof signal ing symbols. How to al locate thepower and other resources between signalingand data symbols to maximize EE is st i l lunknown.

Signaling design considering CSI feedback: Although CSI at the transmitter can help toimprove system capacity, the additional energyconsumption on the overhead of feedback mayslow down the increase of EE. Resource alloca-tion with the feedback of CSI needs further

study.

Figure 5. EE vs. data rate in multihop relay systems when power spectral density

of noise N0 = –174 dBm/Hz.

R (b/channel use)

0.50

1

0

0.5

1.5

2.5

2

3

3.5

4x1020

1 1.5 2 2.5 3 3.5 4

Hop = 1Hop = 2 (7:3), common rateHop = 2 (7:3), common powerHop = 2( 5:5)

η E E

( b / J )



CONCLUSION

In this article, we have comprehensively sur- veyed energy-efficient wireless communicationsfrom the information-theoretic and technique-oriented perspectives. As for the information-theoretic aspect, most literature about EE mainlyfocused on point-to-point scenarios and theimpact of practical issues on EE is not fully

exploited. Thus, research on EE needs to beextended to multi-user and/or multicell cases as well as considering the practical issues such astransmission associated circuit energy consump-tion, which is of great significance to practicalsystem design. As for the advanced techniquesthat will be used in future wireless systems, suchas OFDMA, MIMO, and relay, existing researchhas proved that larger EE can be achievedthrough energy-efficient design. However, most work is still in the initial stage, and more effortis needed to investigate potential topics such asthose listed in this article.

ACKNOWLEDGMENT

This work was supported in part by the ResearchGift from Huawei Technologies Co., Ltd. andthe NSF under Grant No. 1017192. The authors would like to thank the Editor and anonymousreferees for their helpful suggestions that haveimproved this article.

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Figure 6. EE vs. SNR in the worst case scenario for block fading channels withm symbol coherence duration and unit variance when the power spectral density of noise N0 = –174 dBm/Hz. (Please note that SNR is just regular scale and not in dB here.)

m = 50m = 20m = 10m = 5

SNR0

0

1.8

2x1020

1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

2 4 6 8 10

η E E

( b / J )



[30] S. Sayed et al., “Energy Efficiency Analysis of Coopera-tive Access with Relay’s Data Algorithm for Multi-RateWLANS,” IEEE PIMRC ’09, Sept. 2009.

BIOGRAPHIES

GEOFFREY YE LI [F’06] ([email protected]) received hisPh.D. degree in 1994. He was with AT&T Labs — Researchat Red Bank, New Jersey, and is now with Georgia Instituteof Technology, Atlanta, as a professor. His general researchinterests include statistical signal processing and telecom-munications, with emphasis on OFDM and MIMO tech-niques, cross-layer optimization, and signal processingissues in cognitive radios. In these areas, he has published

over 200 papers in refereed journals and conferences, andtwo books; 20 of his publications have over 100 Googlecitations. He also has over 20 patents granted or filed. Hewon the 2010 IEEE Communications Society Stephen O.Rice Prize Paper Award in the field of communications the-ory.

ZHIKUN XU ([email protected]) received his B.S.degree in electronics engineering in 2007 and now is pur-suing his Ph.D. degree in signal and information process-ing, both in the School of Electronics and InformationEngineering, Beihang University, Beijing, China. FromSeptember 2009 to September 2010, he worked as a visit-ing student in the School of Electrical and Computer Engi-neering, Georgia Institute of Technology. His researchinterests include cognitive radio, cross-layer resource allo-cation, and green radio.

CONG XIONG ([email protected]) received his B.S.E andM.S.E degrees from the Department of TelecommunicationEngineering, Beijing University of Posts and Telecommuni-cations, Beijing, China, in 2007 and 2010, respectively. Heis currently working toward his Ph.D. degree with theSchool of Electrical and Computer Engineering, GeorgiaInstitute of Technology, Atlanta, GA, USA. His researchinterests include MIMO, cooperative communications, ener-gy-efficient system design, and cross-layer optimization.

CHENYANG YANG ([email protected]) received her PhDdegrees in 1997. She is a full professor at Beihang Universi-ty, Beijing. She has published various papers and filedmany patents in the fields of wireless communications. Shewas supported by the 1st Teaching and Research AwardProgram for Outstanding Young Teachers of Higher Educa-tion Institutions by the Ministry of Education. Currently,she serves as an Associate Editor for IEEE Transactions on

Wireless Communications, and is chair of the Beijing chap-ter of the IEEE Communications Society. Her recent researchinterests include signal processing in advanced MIMO,cooperative communication, and green radio.

SHUNQING ZHANG ([email protected]) received his B.E.degree from the Fudan University, Shanghai, China in 2005and the Ph.D. degree from the Hong Kong University ofScience and Technology, Hong Kong, China, in 2009,respectively. He joined the Green Radio Excellence in Archi-tecture and Technology (GREAT) project at Huawei Tech-nologies Co. Ltd. after his graduation. His current researchinterests include energy-efficient resource allocation andoptimization in the cellular networks, the joint baseband

and radio frequency optimization, and other green radiotechnologies for energy saving and emission reduction.

YAN CHEN ([email protected]) received her B.Sc.degree from Chu Kochen Honored College, Zhejiang Uni-versity, Hangzhou, China, in 2004 and her Ph.D. degreefrom the same university in 2009. During her Ph.D. study,she has been a visiting researcher in the group of Prof.Vincent Lau in the Department of Electrical and ComputerEngineering, Hong Kong University of Science and Technol-ogy. After graduation, she joined Huawei Technologies(Shanghai) Co., Ltd. and is currently working as a researchengineer in the Green Radio project called GREAT, whichfocuses on energy-efficient solutions for wireless radioaccess networks. Her research interests include green net-work information theory, energy-efficient network architec-ture and management, fundamental trade-offs on greenwireless network design, as well as the radio technologies

and resource allocation optimization algorithms therein.

SHUGONG XU [SM] ([email protected]) received hisPh.D. in 1996. He is currently the director of the AccessNetwork Technology Research Department and a principalscientist of Huawei Corporate Research. Prior to joiningHuawei, he was with Sharp Labs of America, Camas, Wash-ington for seven years and spent a few years working inuniversities including Tsinghua University and City Collegeof New York. His research interests include wireless/mobilenetworking and communication, home networking, andmultimedia communications. He has published more than30 peer-reviewed research papers as lead author in topinternational conferences and journals, of which the mostreferenced one has over 850 Google Scholar citations. Heholds more than 30 granted or pending U.S. patents, tech-nologies of which have been adopted in the WiFi and LTEstandards.

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