Energy Efficiency Maximisation for Cooperative and Non-cooperative OFDMA Cellular Networks … A Survey - ett2850.pdf

TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIESTrans. Emerging Tel. Tech. (2014)

Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/ett.2850

RESEARCH ARTICLE

Energy-efficiency maximisation for cooperative andnon-cooperative OFDMA cellular networksa surveylvaro Ricieri Castro e Souza1*, Jos Roberto de Almeida Amazonas1 and Taufik Abro2

1 Department of Telecommunication and Control Engineering, Escola Politcnica of the University of So Paulo, So Paulo, Brazil2 Electrical Engineering Department, State University of Londrina, Londrina, Paran, Brazil

ABSTRACT

In this survey, techniques to enhance energy efficiency (EE) in orthogonal frequency division multiple access (OFDMA)and orthogonal frequency division multiplexing (OFDM) systems, with or without the utilisation of the cooperativenetwork paradigm, considering also the features provided in the standards of modern cellular wireless networks, suchas LTE-Advanced and WiMAX, are discussed. For the non-cooperative EE maximisation case, we summarise resourceallocation problems and also describe some techniques that can be combined with the basic power/subcarrier allocationproblems. When considering the cooperative OFDM(A) case, we first discuss four basic variables that arise with the relaystation implantation, and after that, other features are also listed, which can be combined with the previously discussedissues. Finally, we review some of the standardisation documents available for fourth-generation systems in order to obtainsystem parameters and simulation scenarios, discuss some methods to analyse and solve the optimisation problems thatcan be proposed with the aforementioned techniques and then point out important open trends and research challenges inthe EE maximisation problem considering OFDM(A) scenario. Copyright 2014 John Wiley & Sons, Ltd.

*Correspondence

. R. Castro e Souza, Department of Telecommunication and Control Engineering, Escola Politcnica of the University of So Paulo,So Paulo, Brazil.E-mail: [email protected]

Received 12 February 2014; Revised 30 May 2014; Accepted 11 June 2014

1. INTRODUCTION

With the increasing number of subscribers and mobilemultitask devices, such as smartphones and tablets, andthe offering of data communication for notebooks anddesktops, the main concern in modern cellular systems isto efficiently provide a high data rate for the served users,which can be translated into increasing the spectral effi-ciency (SE). This can be seen, for example, by comparingthe SE for two cellular systems downlink scenarios, whichevolved from 0.05 bps/Hz in the GSM (2G) systems [1]to a peak SE of 30 bps/Hz for the LTE-Advanced (LTE-A) 11 (4G) systems [2]. Several techniques are utilisedto provide this remarkable gain as, for example, multiple-input multiple-output (MIMO) and adaptive modulationand coding, but this capacity enhancement also comeswith increased power consumption. As pointed out byseveral works, the power consumption for information andcommunications technology is becoming a significant per-centage of the worldwide power consumption [3], and asignificant part of this consumption in mobile communica-tions comes from the radio access network [4, 5], which

has negative impacts for both users and operators: at theuser side, as the battery technologies evolve in a muchslower rate than the offered services [6], the lifetime ofthe battery-powered devices is limited. For operators, theincreased power consumption results in higher operationalcosts [7] while, in [8], is pointed that 50% of the overallexpenses of the service providers is due to base station (BS)powering. Another perspective is the environmental issue,because electricity production results in pollution, as theVodafone case reported in [7]. Indeed, a recent study [8]indicates that information and communications technologyis responsible for 2% of the global CO2 emissions.

In order to balance the increasing data rate and the cor-respondent power consumption, the energy-efficiency (EE)metric has been recently proposed as an important figureof merit for efficient wireless communication networks.Defined as the ratio between the achieved data rate and thepower consumed to provide it, this metric indicates howefficiently the system transforms power into transmitteddata, allowing one to determine the operation point wherethe system transmits more information bits per energy unitor where each information bit has the lowest energetic

Copyright 2014 John Wiley & Sons, Ltd.

. R. Castro e Souza, J. R. D. A. Amazonas and T. Abro

cost, providing an extended lifetime for users and resourcesavings for operators. The EE can be evaluated from theusers perspective or from the whole network, and forboth cases, it is possible to include the power spent withequipments and signal processing, in order to analysethe impact of all components in the system. Consideringonly the EE perspective in the resource allocation problemcan result in significant impact on other important systemperformance metrics [1] as, for example, the spectral effi-ciency (SE). In this way, it is fundamental to the systemperformance to include performance metrics as constraintsin the optimisation problem if they are strictly necessary.

As it is well known, the orthogonal frequency divisionmultiple access (OFDMA) technique is the most populartransmission topology for high data rate communicationsystems, such as WiMAX and LTE-A, which are twopromising candidates for 4G systems [5]. This is due toseveral advantages of OFDMA, such as the robustnessto the inter-symbol interference (ISI), caused by multi-path propagation and specially impactful when symbolrate is high, and the higher diversity dimensions, as wecan consider frequency, multi-user and time dimensionsin the resource allocation strategies [9, 10]. In OFDMA,the total bandwidth is split in narrowband subchannels,which allows to reduce the symbol rate in each subcarrierwhile maintaining the overall data rate. Hence, it is possi-ble to obtain a symbol period higher than the channel delayspread, reducing the negative impact of multipath propa-gation while increasing communication reliability. In thecontext of EE optimisation, the transmission power andsubcarrier allocation are the main optimisation parametersin the literature, but there are other features that can beintroduced, as sleep/active mode switching and modulationorder optimisation.

Despite all advantages of OFDMA systems, oneproblem that any wireless communication system faces isthe channel conditions, mainly represented by fast fadingand intensive path loss, which are inherent to the propaga-tion environment and network topology [10, 11]. The prob-lem of path loss becomes even worse in high-frequencycommunications, as the 5-GHz carrier frequency presentedin both LTE-A and WiMAX and in densely constructedareas, such as metropolitan areas. For the fading case, whenthe channel experiences deep fading, it is impossible orimpractical to maintain communication. In order to reducethese problems, one of the most promising techniquesis the cooperative communications. Under this paradigm,relay stations (RSs) are placed at the cell to improve cov-erage and/or capacity, mainly for users in coverage holesor cell-edge area, retransmitting the received signal frommobile stations (MSs) or BSs to destination. In this way, itis possible to reduce the effects of path loss and form a vir-tual antenna array (such as virtual MIMO) [10] to providespatial diversity, providing robustness to deep fading whilereducing power consumption, which can be translated intoEE improvement. When considering EE optimisation forcooperative OFDMA, various aspects can be optimised,

such as time/frequency sharing, relay placement andretransmission protocol.

Under the perspective of multiple possible approachesfor EE maximisation, this survey reviews basic concepts inOFDMA systems, cooperative communications and energyefficiency, in order to effectively discuss representativeEE maximisation techniques and to provide an interestingtool to evaluate/analyse EE in 4G cellular systems. Thesurvey is organised as follows: In Section 2, we presentthe OFDMA system model, cooperative networks tech-niques and the EE optimisation approach, as well as theterminology deployed in this survey. In Section 3, wedescribe methods for EE maximisation in non-cooperativeOFDMA networks, while in Section 4, we do the samefor cooperative OFDMA systems. In Section 5, we dis-cuss methodologies for simulation and evaluation methodsfor EE optimisation problems, also, a brief guideline forsystem parameters and scenarios choices based on a listof documents from different standardisation workgroups isprovided. Finally, Section 6 concludes this survey and putinto perspective the principal research challenges and opentopics of interest.

2. SYSTEM DESCRIPTION

This section gives an overview of the main techniques usedin 4G systems and discussed in this survey. Although theEE paradigm is proposed for several multiple access (MA)techniques and network topologies, only the OFDMAmodel is presented because the focus of this survey is4G-based systems. In this way, we provide an overviewOFDM/OFDMA systems, cooperative networks and theEE definition.

2.1. OFDM/OFDMA

The orthogonal frequency division multiplexing (OFDM)technique consists in splitting a user-data stream intoseveral substreams, which are sent in parallel on severalsubcarriers, obtained by splitting the total bandwidth innarrower channels. Considering that X is the set of symbolsto be transmitted, each symbol Xi modulates the ith sub-carrier, with jXj 6 N, where N is the number of availablesubcarriers. The rationale of this approach is to increasethe individual symbol time (Ts,i) in each subcarrier withoutincreasing the total time to transmit X (Ts), in such a waythat Ts,i is higher than the channel delay spread Td , which isfundamental to reduce the effects of ISI, without affectingthe data rate that remains the same [9]. The symbol time ineach subcarrier is given by Ts,i D NTs, resulting that N isdefined in order to achieve Ts,i ! Td , that is, N ! Td=Ts.

Figure 1 shows an OFDM block diagram and illustratesthe overall process. The symbols are modulated in thefrequency domain by taking the N-point inverse discreteFourier transform (IDFT) of X. As the data stream X isserial, it first passes through the serial-to-parallel converter,then the IDFT is applied, obtaining the time samples x,

Trans. Emerging Tel. Tech. (2014) 2014 John Wiley & Sons, Ltd.DOI: 10.1002/ett


Figure 1. OFDM system block diagram, where X is the set of symbols to be transmitted, h is the multipath channel coefficients andbX is the set of estimated symbols.

which are serialised again by the parallel-to-serial con-verter. After that, the data stream is transmitted over awireless channel with impulse response given by h Dh0, h1, " " " , hj, " " " , hv!, where v is the length, in samples,of the channel delay spread.

At the detector side, the received time signal y passesthrough a serial-to-parallel converter, then the DFT isapplied, and the frequency samples Y are serialised anddetected, generating the estimated symbols vector bX. Notethat in the OFDM transmission technique, the various sub-carrier signals are generated digitally and jointly by aninverse fast Fourier transform algorithm in the transmitter,and their spectra strongly overlap on the frequency axis.This being so, generating the transmit signal is simpli-fied, and the bandwidth efficiency of the OFDM/OFDMAsystems is significantly improved.

As the DFT/IDFT is used, it is necessary that the signaland channel convolution be circular, which implies that xor h has to be periodic. On the other hand, despite Ts,i !Td , it is possible that some symbols are still affected bymultipath propagation [9]. In order to overcome the effectsof these two situations, a cyclic prefix (CP) is inserted,which consists of copying the Ng > v last (first) samples ofx to the beginning (ending) of the OFDM symbol, whichequates the linear and circular convolutions results. Also,the CP insertion makes the received signal ISI-free. Con-sidering that the signal at the source has N C Ng samples,the convolution with the fading channel results in a signalwith N C Ng C v samples. By discarding the first Ng sam-ples, which are corrupted by the delayed samples of the

previous signal, and the v last samples of the received sig-nal, which interfere with the next OFDM symbol, there areN samples that are ISI-free at the cost of a data rate penaltyimposed by the redundancy. In summary, the CP is insertedafter the IDFT at source-side and removed before the DFTat receiver-side.

The main parameters associated to the OFDM systemare (i) number of subcarriers (N), which must satisfy thechannel delay spread constraint; and (ii) CP size, whichmust be at least equal to the number of channel multipathcopies.

Despite all the aforementioned advantages, there aretwo main drawbacks in OFDM systems [9, 12]: the highlyprecise frequency synchronisation needed and the peak-to-average power ratio (PAPR). The first one is causedby the sinc functions in frequency domain at the detec-tion, because the inter-carrier interference (ICI) is zeroonly when the frequency is perfectly synchronised; thissynchronism can be lost because of the imperfect oscil-lators and the Doppler effect, generated by user mobility.The second one is intrinsically caused by the multicarrierstrategy. In time domain, the OFDM symbol is composedby many narrowband signals, which could result in peaksof power much larger than the average power, makingthe power amplifier to operate in the nonlinear region,incurring in distortion and loss of efficiency. As describedin the following, the OFDMA technique results to bethe solution for the second problem, at least under theusers perspective.



Figure 2. Basic cooperative strategies.

As OFDM is a multiplexing strategy, it can be com-bined with different MA dimensions, as time division MA,code division MA (CDMA) or frequency division MA(FDMA). Because the 4G standards consider the MA infrequency, we will focus on the OFDMA system model. InOFDMA, for each user is allocated a number of subcarri-ers in a given time slot, which are updated in the followingslot [9]. In this way, it is possible to recognise at least twodiversity dimensions: time and multi-user. The time dimen-sion comes from the fact that if the channel quality is poorfor a user in all subcarriers in a given time slot, it is possibleto make this user wait for another time slot. The multi-user dimension can be explored from the perspective ofthe choice of which users allocate which subcarriers, givena specific metric, such as, for example, the instantaneouschannel state information (CSI).

Because in the OFDMA system the users compete to usethe subcarriers, it is necessary to define for each allocationperiod the user-subcarrier mapping and the transmissionpower for each user in the mapped subcarriers. On thedownlink, BS defines the mapping and then transmits thisinformation to the active users via control messages, sothat each user only decodes the information on the allo-cated subcarriers. On the uplink, there are two possibilities:for distributed solutions, the users decide the subcarrierallocation, and each one of them must inform the BS theallocated subcarriers; while for centralised solutions, theBS proceeds as in the downlink case.

Depending on the metric(s) to be optimised, that is,data rate maximisation, power consumption minimisationor fairness provision, different subcarrier, time and powerallocation algorithms can be deployed. The algorithms arenot specified along the OFDMA model or standards [9],and each implementation must decide how to deal withthe associated problem. If we consider K users, N sub-carriers, time slot equal to the symbol time Ts and powerp 2 0, Pmax!, where Pmax is the maximum allowed trans-mission power, the allocation problem becomes extremelycomplex to be optimally solved in real time. That beingso, commercial systems such as LTE-A and WiMAX usetime slots with multiple symbols and group subcarriers toform subchannels, reducing the number of possible combi-nations and, in consequence, the computational complexityof the optimisation problem [9].

It is well known that the power amplifier of mobiledevices, due to cost reasons, cannot be as efficient as thosedeployed in the BSs, making the uplink PAPR quite sig-nificant. The OFDMA system reduces the impact of thePAPR problem in the uplink of the OFDM system [9].In OFDMA systems, each user uses only a portion of theavailable subcarriers, and because the PAPR is proportionalto the number of used subcarriers, each user experiments alower PAPR and is able to use less power than when eachuser uses all subcarriers in only one time slot.

2.2. Cooperative cellular networks

In order to overcome the destructive nature of wirelesschannels, one of the most promising techniques is thecooperative communications paradigm. In this scenario,one or more equipments, called relays, retransmit thesignal received from the source to the destination, pro-viding spatial diversity if the source-destination link isavailab1le or improving coverage otherwise. As the pathloss is nonlinear and inversely proportional to the source-destination distance [11, 13], if the user deploys relay(s)in a multi-hop communication, the power spent with prop-agation losses can be reduced substantially, increasing themobile terminal, as well the overall system, efficiency interms of power consumption. The spacial diversity canbe seen as virtual MIMO [11], where the relays, plusthe direct transmission, act as virtual antennas. This is aninteresting method to provide spatial diversity in cellularcommunications, allowing the increase of the cell coverage(macro-diversity); moreover, because the required antennaseparation to provide uncorrelated signals for MIMOsystems is at least half wavelength (micro-diversity) [9],in cases where carrier frequency is in the hundreds ofmegahertz range or less, it becomes unrealistic to obtainuncorrelated signal replicas in mobile cellular devices,such as cell phones.

A simple example of a cooperative communication canbe seen in Figure 2, where the source node S communicates

Considering that the path loss is commonly defined as Lod!npl , whereLo is a constant depending on system parameters, d is the communica-tion distance and npl is the path-loss exponent, which depends on thepropagation scenario, and in general 2 6 npl 6 6.



with the destination node D using a single-relay node RS.There are several ways to do it [10]:

(1) S transmits to D in the first time slot, and RS over-hears and then retransmits in the second time slot(Figure 2(a)).

(2) S transmits to RS in the first time slot, and then RStransmits to D in the second time slot (Figure 2(b)).

(3) S transmits to D in the first time slot, RS transmitsthe overheard message in second time slot and S alsotransmits a new (or copied) message in the secondtime slot (Figure 2(c)).

(4) S transmits to RS in the first time slot, and bothRS and S transmit to D in the second time slot(Figure 2(d)).

Another classification of RSs concerns the type of equip-ment [14]. If the equipment is dedicated to retransmissionand installed as part of the cell infrastructure, it is calledfixed RS. On the other hand, if the user equipments areable to retransmit information of other users, they arecalled mobile RSs. Although both implementations arepossible, the fixed approach is the choice for the 4G stan-dards, given that there are dedicated power supply anddedicated equipment for relay operation, while the mobiledevices would have to share limited power and time toact as relay node, despite the higher diversity that couldbe achieved in this configuration. Another advantage offixed relays is that they can be installed in a plannedway, aiming to provide better coverage to otherwiseuncovered holes or to provide higher data rates where itis necessary.

Based on Figure 2, it is possible to infer that the relaycan either be recognised as a network equipment by BSsand MSs or not [15, 16]. The first condition, presentedin Figure 2(b) and (d), is called non-transparent relaying.In this way, the RSs are considered as a BS, communi-cating directly with the mobile users. This mode is usedto extend coverage, because the link MS-BS is unavail-able [17]. The second mode, presented in Figure 2(a)and (c), is called transparent relaying and correspondsto the case where MSs and BS communicate directly,and the RSs just overhear the transmission and thencollaborate by retransmitting. This model is used toincrease throughput/reliability or to reduce power con-sumption, because the MSs are already covered by theBS [17].

To retransmit the information, there are also two types ofprotocols [10, 18]. The first one refers to the case in whichthe RSs decode the received signal before retransmit-ting it, called regenerative protocols, while in the secondcase, the signal is analogically processed before retrans-mission, being called non-regenerative protocols. The bestknown implementations are the decode-and-forward (DF)and amplify-and-forward (AF) protocols, respectively. Themain advantages of regenerative protocols are the pos-sibility of more sophisticated processing, including re-modulation and coding changes, aiming to not propagate

noise and interference from the received signal to the nextnode, while the drawbacks are the processing cost, extralatency and possibility of wrong detection. As pointedout by Laneman [19], the AF protocol presents a higherdiversity order than the DF protocol.

Finally, there is the method to separate the backhauland access links in some dimension, because these linkscannot operate in the same time/frequency/space with-out interfering with each other. As shown in Figure 3,there are three different links in cooperative communi-cations: the direct link, where MS and BS communicatedirectly; the backhaul link, where RS and BS communicateand the access links, where MS and RS communicate toeach other. Furthermore, because the backhaul and accesslinks cannot operate in the same time/frequency/spacebecause of self-interference [20], as depicted in Figure 4,it is necessary either to separate these links in onedimension or to deal with the self-interference. The pos-sible solutions to avoid the self-interference are [21]as follows:

(1) Inband relaying: backhaul and access links are sep-arated by sharing the resources in time domain, orby sufficient antenna separation at RSs, to avoidself-interference.

Figure 3. Link scheme for cooperative communications.

Figure 4. Self-interference (or loop-interference) when accessand backhaul links are not well isolated: (a) downlink and

(b) uplink.



(2) Outband relaying: one RF channel is dedicated toeach link, or the original RF channel is split in twofixed portions of the total bandwidth.

Regarding the commercial scenario, both LTE-A andWiMAX mainly consider fixed relays and regenerativeprotocols [18, 21, 22], because the adaptive modulation andcoding (AMC) feature is only possible with the deploy-ment of regenerative protocols. Besides, for LTE-A, bothtransparent and non-transparent modes are allowed, andalso inband and outband relaying, the last with a second RFchannel. Just to clarify the common LTE-A nomenclature,in this case, there are two types of RSs: the type-I RSs arenon-transparent equipments, deploying regenerative proto-cols and operating at the MAC layer, just like a regularBS from the MSs point of view, while the type-II RSs aretransparent equipments that can operate with regenerativeor non-regenerative protocols [23].

When the self-interference is accepted, techniques asfull-duplex inband relaying and two-way relaying can beadopted. The use of full-duplex inband RSs has beencogitated in two agenda items of 3GPP [24, 25], wherethe self-interference is handled by antenna isolation andinterference cancellation. When using full-duplex inband,access and backhaul links operate at the same time in thesame direction: for downlink, we have BS-RS and RS-MSlinks operating at the same time, while in the uplink, wehave MS-RS and RS-BS transmissions at the same time.This method has obvious advantages over the half-duplexinband and full-duplex outband methods. For the formercase, the system uses two time slots to transmit the samedata; as a result, the capacity is halved, while for the lattercase, the extra required carrier halves SE. In the full-duplexinband method, there is only one carrier to use, and it isnecessary to use only one extra time slot, which may beconsidered irrelevant overhead, given a sufficiently largenumber of transmissions.

In [24], it is stated that a full-duplex inband relayingcan be specially applicable when the RS-BS antenna hasa natural isolation to the MS-RS antenna as, for example,in subway stations and densely populated buildings as, forexample, shopping centres, where the MS-RS antenna canbe installed inside the building and the RS-BS antenna out-side. The main drawback listed in [24] is that if the RSis installed far from the BS as, for example, near the celledge, the BS-RS signal can be several orders of magni-tude lower than the signal transmitted by the RS to theMSs, resulting that even if most of the self-interference iscancelled, the residual interference level can be compara-ble with the BS-RS power level. As described in [24], theinterference cancellation at the RS can achieve an excellentresult once:

Recently, mobile relaying scenario, such as train relaying communi-cations, has been considered in LTE-A and WiMAX standards.

# the RS perfectly knows the interfering signal, as it istransmitting it;

# the control symbols and fed-back signals can be usedfor channel estimation;

# the channel between the antennas is static.

The previous considerations suppose that the RS has twoantennas and use one to transmit and another to receive.However, in [26], the two antennas are used both to receiveand to transmit as well.

The two-way relaying is a method originally used fordirectly connecting two MSs via an RS. In the first timeslot, both MSs transmit to the RS, which processes thereceived signal and then retransmits a combined ver-sion of the signal to both MSs in the second time slot.Because the MSs know the data they transmitted in thefirst slot, it is possible to use it for interference cancel-lation and then obtain the data transmitted by the otherMS. Because the MS-BS link can be seen as a two-waylink, this concept can be extended to the cellular scenarioin order to reduce the number of necessary time slots[27] and, consequently, improve system performance interms of both energy and SE. The two-way relaying canbe also carried out in three time slots, if the interferencein the first slot is prohibitive or may result in signifi-cant loss in performance. Two-way relaying analysis inOFDM systems can be found in [28] and [29], and Figure 5illustrates the half-duplex inband, full-duplex inband andtwo-way relaying modes and the respective number oftime slot necessary to complete a downlink and uplinkcooperative transmission.

2.3. Energy efficiency

The EE metric is used to evaluate the efficiency with whichthe communication system converts consumed energy intoeffective transmitted data. This evaluation changes theparadigm from the data rate efficiency, mainly measuredby SE, to the cost of the data rate in terms of power con-sumption. The basic EE metric units are bits per joule,that is, the number of transmitted effective bits per jouleconsumed, and joules per bit, that is, the amount ofenergy consumed to transmit one effective bit. In this con-text, an effective bit refers to a bit that carries effectiveinformation from the source to the destination, discard-ing protocol headers, signalling data and redundant bitsinserted by error detection codes. The EE metric can beevaluated as

" D GP

!bits

joule

"(1)

where G is defined as the effective throughput, or goodput,and P is the total power consumption.

Considering the bit-per-joule definition, there are twomain approaches to model the energy-efficiency problem.



Figure 5. Inband relaying modes and the necessary slots to complete a downlink and an uplink transmission.

The first one considers that the system provides a fixed datarate, and then an efficiency function f ."/ accounts for pos-sible bit/symbol errors. In general, this efficiency function

depends on the instantaneous signal-to-interference plusnoise ratio (SINR) # between source and destination,resulting that " can be modelled as



" D

KPkD1

NPnD1

`.k,n/f .#.k,n//r.k,n/

KPkD1

NPnD1

%p.#.k,n// CKP

kD1pc.k/

!bits

joule

"(2)

where in the context of OFDM(A), `.k,n/ is the proportionof the number of information bits L to the total numberof bits M, which discards bits used for CP and coding oneach subcarrier, because the coding can be different in eachsubcarrier; #.k,n/ is the instantaneous SINR for the kth useron the nth subcarrier; r.k,n/ is the fixed rate that can beprovided to the kth user on the nth subcarrier in bits persecond; p.#/ is the necessary transmission power in orderthe link on the nth subcarrier to achieve a given SINR # , inWatts; % is the power amplifier inefficiency and pc.k/ refersto the circuit consumption in Watts.

The SINR for the kth user on the nth subcarrier can becalculated as

#.k,n/ Dp.k,n/jh.k,n/j2Now C I.n/

(3)

where jh.k,n/j2 is the channel gain for the kth user on thenth subcarrier, which includes the fast fading and path-loss effects; w is the subcarrier bandwidth, in Hertz; No isthe power spectral density of the additive Gaussian whitenoise, in Watts per Hertz and I.n/ is the interference fromother users on the nth subcarrier, which can be origi-nated by imperfect frequency synchronism in the detector,subcarrier sharing or multicell interference.

The efficiency function is used to discard receptionerrors, because the EE numerator refers to goodput. Ingeneral, this function approximates the package successrate [30], because using the exact formulation implies thetrivial solution p.#/ D 0 [31] when the circuit power isnot taken into account. As f .#/ is a cumulative densityfunction (CDF), the two main constraints are

f .#/ D#

0, # < 01, # ! 1 (4)

To avoid the trivial solution p.#/ D 0, it is definedthat f .0/ D 0. The parameters of f .#/ must reflectthe system model in order to obtain suitable results. Forinstance, when considering low-order phase-modulation(binary phase shift keying, quadrature phase shift keying)and additive white Gaussian noise, the approximationf .#/ D .1 $ e!! /M is a well-known choice [31, 32].

The second approach is to consider the data rate as afunction of the achieved SINR, including or not the effi-ciency function in the formulation. In this case, the EEfunction " is generally given by

" D

KPkD1

NPnD1

`.k,n/r$#.k,n/

%

KPkD1

NPnD1

%p.#.k,n// CKP

kD1pc.k/

!bits

joule

"(5)

where f .#.k,n// D 1 is omitted and r$#.k,n/

%is defined in

terms of the Shannon capacity equation [33]:

r.#.k,n// D w log2.1 C #.k,n// bits/s! (6)

As the Shannon capacity is an upper bound of the achievedcapacity, in general, an SINR gap {.k/ is considered toaccount for this limitation [34], resulting that the data ratecan be rewritten as

r.#.k,n// D w log2.1 C #.k,n/{.k// bits/s! (7)

with

{.k/ D$1.5

ln .5BERk/, 0 < {.k/ 6 1 (8)

where BERk is the maximum tolerable bit error rate for thekth user.

The circuit power pc can be modelled by a static compo-nent, which accounts for the power consumed even whenthe equipment is not transmitting, and a dynamic part,which depends on the current data rate, as discussed in [35]and [36]. In general, only the fixed amount of power spentindependently of any transmission has been consideredin simple power consumption models. Indeed, consider-ing the circuit power consumption in the EE maximisationcontext tends to make the system to transmit at higherdata rates, in order to dilute this fixed power consumptioneffect. Parameters as % and pc are system dependent andcan be obtained in standards or approximated given certainspecifications, such as cell size, deployment scenario andfunctionalities, as discussed in [36].

It is worth noting the difference between the powerminimisation and EE maximisation problems. The powerminimisation metric focuses on minimising the allocatedpower in order to achieve a given minimum data rate,while the EE problem focuses on determining the allocatedpower that results in the highest ratio between transmitteddata and consumed energy. When the problem has no min-imum rate constraints, it is possible that certain users orsubcarriers have been put in outage because the instanta-neous channel conditions are not good enough in a specifictime slot. Even for the rate constrained problem, if a userhas a good channel quality, it is possible that its data rateis much higher than the minimum rate criterion, resultingthat the optimum power allocation for the EE criterion isnot necessarily the minimum consumption obtained fromthe power minimisation problem. Another example comesfrom the increased circuit power situation: from the per-spective of the power minimisation problem, the circuitpower does not affect the allocated transmission power,while under the EE maximisation, perspective increasingcircuit power increases the allocated data rate and, con-sequently, the allocated transmission power in order todissipate the constant term.

The earlier works in EE focused in maximising theEE without taking into account quality of service (QoS)constraints [3032], such as data rate and delay. As



QoS is fundamental in modern cellular standards, recentworks include such metrics as constraints, investigatingthe impact of them and proposing techniques to opti-mise EE, considering QoS aggregation and QoS overtime, that is, maintaining QoS over time slots and notnecessarily instantaneous QoS. The impact of using EEmetrics is also analysed in terms of trade-offs betweenwell-established metrics, such as spectral and EE (EE-SE)trade-off [3739], operational/deployment cost, allocatedbandwidth [1] and delay constraint [40].

2.4. Notation

Tables I and II present the terminology used in this surveyfor the acronyms and symbols, respectively.

3. EE IN OFDM/OFDMA

As OFDMA is the main MA technique for 4G cellularsystems and EE is becoming an important system evalu-ation criterion, there are several works in literature con-sidering EE maximisation in OFDMA. The works mainlyfocus on allocating subcarriers and transmission power toobtain higher EE, using frequency, multi-user and/or timediversities in the algorithms, which is in general named

scheduling algorithm. As the power/subcarrier (or evensubchannel) allocation problem is non-polynomial-hard[41, 42], in general, suboptimal strategies have been usu-ally proposed. Some insights about resource allocation inOFDMA networks can be found in [43], where a survey onuplink resource allocation for OFDMA systems is carriedout. Despite the fact that EE is not taken into account, someof the conclusions can be used to plan the EE resource allo-cation framework. Examples of that include the impact ofbuffer model, instantaneous or ergodic QoS metrics, howto define system data rate (if continuous or given by theAMC), as well as individual subcarrier allocation or con-sidering a group of subcarriers or even the resource blocks(RBs), which are portions of subcarriers and time.

As an example of suboptimal approaches, the authorsin [42] describe the optimal RB/power allocation problemfor the uplink of a single-cell OFDMA system and findthat the complexity is about O $KNKRB

%, where NRB is the

number of resource blocks per allocation interval. As thiscomplexity is prohibitive for commercial implementations,the idea behind the paper is to develop iterative subopti-mal algorithms, which allocate one RB per time, repeatingthe process until there are no RBs and available users toallocate to them. Two algorithms are developed to iter-ate over the available RBs, one considering a determinateRB order and the other evaluating what is the best RB

Table I. Table of acronyms.

Acronym Description Acronym Description

AF Amplify and forward MAC Media access controlAMC Adaptive modulation and coding MAI Multiple access interferenceARQ Automatic repeat request MCS Modulation and coding schemeBER Bit error rate MIMO Multiple-input multiple-outputBS Base station M-QAM M-symbol quadrature amplitude modulationBPSK Binary phase shift keying MRC Maximal-ratio combiningCAPEX Capital expenditure MS Mobile stationCDF Cumulative density function NP Non-polynomialCDMA Code division MA OFDM Orthogonal frequency division multiplexingCF Compress and forward OFDMA Orthogonal frequency division MACoMP Coordinated multipoint OPEX Operational expenditureCP Cyclic prefix PAPR Peak-to-average power ratioCSI Channel state information PSR Package success rateDF Decode and forward QoS Quality of serviceDFT Discrete fourier transform QPSK Quadrature phase shift keyingDS-CDMA Direct-sequence CDMA RB Resource blockEE Energy Efficiency RF Radio frequencyFDMA Frequency division MA RO Relay orderingGSM Global system for mobile communications RS Relay stationHARQ Hybrid ARQ SE Spectral efficiencyICI Inter-carrier interference SER Symbol error rateICT Information and communications technology SISO Single-input single-outputIDFT Inverse DFT SF Shorten and forwardIRI Inter-relay interference SG Stochastic geometryISD Inter-site distance SINR Signal-to-interference plus noise ratioISI Inter-symbol interference SNR Signal-to-noise ratioLTE(-A) Long term evolution (-advanced) TDMA Time division MAMA Multiple access WiMAX Worldwide interoperability for microwave access



Table II. Table of symbols.

Symbol Description Symbol Description

Factor of relay placement adjust O../ Asymptotic complexity order! SINR or SNR, depending on definition P Total power consumption!TH Minimum ! to cooperate p Allocated transmission power" Energy-efficiency utility function p.!/ Power consumption to achieve !% Power amplifier inefficiency pc Circuit powerd Distance between two nodes Pmax Maximum power allowed for a given userf.!/ Efficiency function R Cell radiusG Effective throughput (goodput) r Data rateh Channel impulse response vector Ts,i Time of each symbol Xihi ith channel impulse response Ts Time to transmit XK Number of users Td Channel delay spreadL Number of information bits per packet v Length, in samples, of the channel delay spreadLo Path-loss constant W Available bandwidthM Number of bits per packet X Set of symbols to be transmittedN Number of subcarriers x Signal to be transmitted in time domainnpl Path-loss exponent Xi ith individual symbol to be transmittedNg Size of the cyclic prefix Y Signal received in frequency domainNRB Number of resource blocks y Signal received in time domainNs Number of subchannels

to be allocated in the current iteration. Numerical resultsshow that the proposed algorithms can provide a subopti-mal EE with lower complexity, but a gap higher than 10%is obtained between the EE optimal solution and the pro-posed algorithms when the number of users and subcarriersare increased, mainly for the second parameter.

In [5], the authors formulated the EE maximisationproblem for single-cell OFDMA systems in both uplinkand downlink, considering as QoS metrics a minimumindividual or total rate criterion, respectively. In the down-link description, weights are used in data rate to providefairness/priority, while a max min optimisation criterion inthe uplink case has been considered in order to optimisethe lowest individual EE. The EE maximisation in down-link is modified to an equivalent max min problem, whichis similar to the uplink model, and suboptimal algorithmsare presented. For both modes, the algorithms have twophases: first, they virtually allocate the worst subcarrier toeach user, that is, this process is considered as virtual allo-cation because the worst subcarrier is not really allocatedto the corresponding user, but only used for the urgencymeasurement. In fact, more than one user can be virtuallyallocated to the same subcarrier, also, this subcarrier canbe allocated for a user that has not been considered at thebeginning of the algorithm and then iteratively choose theuser with the lowest EE and allocate the best subcarrier forthis user, updating the achieved EE. The results obtainedwith the suboptimal approach are close to that obtainedwith the optimal solution, which has been achieved by test-ing all possible configurations of subcarriers and using awater-filling algorithm to allocate power for each config-uration, where the optimal water level for EE is obtainedwith a bisection method.

Maintaining the single-cell scenario and considering thedownlink, the authors in [44] describe an alternative way

to increase EE. Including the circuit power term from MSsin receiving mode on the EE formulation, Equation (1),the problem now consists in minimising the time that theMSs are in active mode, so that the transmissions for eachuser is concentrated in few time slots and the power spentin receiving mode is saved. After time-slot allocation, apower control algorithm allocates the necessary power toeach user, which can be changed to consider other metricswithout affecting the first algorithm, as pointed out by theauthors. So, it is possible to adapt an algorithm such as theone in [5] to further improve EE.

In [41], it is considered a multicell downlink OFDMAscenario with AMC, where the allocated subcarriers areswapped in an intermediate step in order to guarantee QoSto more users, and then the power allocation is rerun,verifying if it is useful to change the modulation andcoding scheme between two subcarriers allocated to thesame user, in order to reduce the consumed power and,consequently, the generated interference. Reducing theinterference, other users can reduce transmission powerover those subcarriers, which can lead to an overallpower reduction, called as virtuous loop. The algorithmpresents significant gains in terms of spectral and energyefficiencies regarding conventional approaches, such asround-robin scheduling.

Maintaining the multicell scenario, the authors in [45]also consider the problem of subcarrier and power alloca-tion for maximising EE but with the N subcarriers groupedinto Ns subchannels and deploying pricing strategies. Aspointed out in [46], pricing mechanisms are an effectiveway to reduce transmission power and therefore multicellinterference, resulting in higher EE. The subchannel/powerallocation is split in two steps: in the first one, equalpower is considered, and each subchannel is allocated tothe user with highest SINR. To avoid underserved users



and improve fairness, at the final of this step, an adjust-ment is made to distribute subchannels from users with twoor more subchannels to users with any assigned subchan-nel. The second step consists in allocation of power to theassigned subchannels, considering the bits per joule metricwith pricing coefficient.

The joule-per-bit metric is used in [4], where a single-cell downlink OFDMA system case is investigated. As theauthors discuss, referencing [47], the bit-per-joule metricpresents lack of linearity between the consumed energyand the transmitted power, which can lead to incorrectconclusions; in spite of this, the bit-per-joule metric iswidely adopted. The problem consists in allocating sub-carriers and bits (consequently power) to each subcarrierin order to provide a minimal SE to the system and mini-mal data rate for each user, considered as the QoS metrics.The problem is formulated using a set of modulations, thatis, discrete data rate, and the fractional problem devel-oped is solved using Dinkelbachs parametric approach, inan iterative way. The interesting result presented is thatjoule-per-bit decreases when system loading increases, thatis, the system becomes more efficient when system load-ing increases. This can be explained as follows: (i) withmore users, higher data rate is necessary, which reduces thenegative impact of circuit power consumption at the BS;(ii) a higher multi-user diversity can be achieved with moreusers; and (iii) the low number of users considered initially.The EE-SE trade-off is investigated by increasing the min-imum data rate per user and, as a consequence, the overallSE; numerical results show that increasing the minimumdata rate/SE requirements, the EE is reduced.

All the aforementioned works consider an OFDM/OFDMA system where the users do not share the samesubcarrier/subchannel at the same cell and time. In order toinvestigate if that is the optimum scenario for EE maximi-sation, in [48], it is considered the downlink of a single-cellsingle-input single-output (SISO) OFDM system wherethe users are able to share the subcarriers, introducingMA interference. Analysing the proposed problem, theauthors conclude that even with the possibility of subcar-rier sharing, the optimum solution is that only one useruses each subcarrier at a given time, which corresponds tothe OFDMA approach. With this information, the problemis simplified to the OFDMA case, and numerical resultsare obtained by simulation, without any QoS guarantee.It is also presented a simplification to obtain the optimalpower/subcarrier allocation, using bisection method overthe transmission power to solve the optimisation problem.

There are also other techniques that could be deployedwith OFDMA to further improve EE. One of them is thecoordinated multipoint (CoMP), which allows the BSs inneighbour cells to jointly define which users each one willcover and also which subcarriers will be allocated, aimingto improve coverage and reduce interference, while pro-viding ways to reduce transmission power and improveEE. Another common technique is MIMO, where the sta-tions are equipped with multiple antennas, for transmission

and/or reception, in order to provide spatial diversity and/ormultiplexing gains.

In the CoMP technique, BSs from neighbour cells areconnected to a central unit, generally by high-capacitylinks, which processes the information sent by these BSsin order to determine which station, or stations, will beallocated to a given user and also the subcarrier(s) wherethis transmission will occur. According to [8, 49], CoMPtechniques can be classified in two types:

(1) Joint processing: in this scheme, one user isselected to receive/transmit in a given time/frequencyresource, and then a group of BSs (joint transmis-sion) or only one (dynamic cell selection) is selectedto operate with this user. When a group of BSs isselected, it is possible to exploit diversity, whichcan result in significant reliability gain or cover-age improvement to cell-edge users, while selectingone BS at each transmission also exploits macro-diversity, because it is possible to choose the BSwith the best channel conditions; this is similar to theopportunistic relaying technique described in [50].

(2) Coordinated scheduling/beamforming: the transmis-sions are coordinated between the BSs in order toreduce the inter-cell interference, with only one BStransmitting to each user. In this way, there is no user-data exchange between the BSs, only the channelinformation is transmitted to the central unit in orderto decide the resource allocation. The cell-edge userscan receive less interference and, as a consequence,improve performance.

These techniques are compared in terms of EE for anLTE-based downlink scenario in [8], considering threewell-known schedulers: maximum carrier-to-interferenceratio, round robin and proportional fairness. In thedeployed scenario, it is demonstrated that the dynamic cellsolution presents the highest EE for all the consideredschedulers, followed by joint transmission and coordi-nated scheduling/beamforming. In terms of the schedulers,the maximum carrier-to-interference ratio scheduler resultsin the highest EE for all the techniques, which can beexplained by the better exploitation of multi-user diversity.

As discussed in [51], system capacity in CoMP sys-tems is directly proportional to the backhaul link capacity,which implies that the best performance is obtained withinfinite capacity backhaul link to exchange the necessaryinformation about users, including CSI and data to betransmitted. Obviously, the deployment and energy costof such backhaul links, even if they are dedicated to thisfunction, is prohibitive, so it is necessary to determine theimpact of limited backhaul capacity. Thus, in [51], eachBS has a limited backhaul link and needs to decide withwhich BSs are the best to cooperate with in order to reduceinter-cell interference and maximise capacity. By consid-ering power/subcarrier allocation, zero-forcing precodercoefficients, fairness and backhaul allocation, a heuristicalgorithm is developed, which first allocates the backhaul



link and then iterates over the user scheduling consideringpower/precoder fixed and power/precoder optimisation forfixed scheduling.

From the EE perspective, limited backhaul resourceallocation is investigated in [52], considering multicelldownlink OFDMA and a central unit that processes theinformation received from all BSs and then communicatesto each one, exchanging the necessary data through lim-ited backhaul channels. With this limitation, the trade-offbetween EE, backhaul capacity and network capacity areanalysed considering that, for each user, must be assured aminimum data rate, and the backhaul link presents a max-imum data rate constraint, while power, subcarriers (userselection) and zero-force beamforming have been consid-ered as optimisation parameters. The optimisation problemis solved in three steps, one for each variable, performedin an iterative way. It is analysed the EE behaviour underdifferent maximum transmission power, backhaul capaci-ties and number of active users. Interestingly, it has beenshown that EE is not directly proportional to backhaulcapacity, because of the power consumption cost nec-essary to increase backhaul capacity that overcomes thecapacity gain.

For MIMO systems, there are several considerationson energy-efficient system design. For example, in [53],the authors analyse the SNR gains obtained with multi-ple antennas in both receiver and transmitter equipments,considering different cell radius scenarios and number ofantennas. Numerical results show that for an increasingnumber of antennas and same cell radius, the SNR gainis substantially increased. However, because the energy-efficient design must consider operational costs as, forexample, circuit power and computational complexity,these results require a more accurate analysis. One caseis analysed in [54], considering MIMO deployment in apico-cell scenario. In this case, the results demonstratedthat the power consumption is increased for the same SNR,which results in lower EE. As multiple antennas in mobiledevices can be hard or even impossible to implement,considering multiple antennas only for the BS equipmentis also an interesting scenario, as the case described in[55]. Indeed, numerical results show that when the num-ber of antennas is increased, the average EE for the MSsis improved, and a significant gain is achieved when thenumber of MSs grows. Also, the jointly power/subcarrieroptimisation procedure promotes an improvement in theEE when compared with only the adoption of powerallocation procedure.

An important consideration in MIMO systems is theimpact of circuit power consumption, as discussed in [56].It is well known that MIMO systems reduce the necessarypower to achieve a given bit error rate (BER) requirementbecause of the diversity/array gain, but the extra circuitcomponents necessary to operate a MIMO system canreduce or even nullify the transmission power reductionbenefit. Hence, for a fixed rate/modulation system, there isa breakpoint distance for which a MIMO system outper-forms a SISO system. To reduce the negative impact of the

circuit power consumption, the authors propose to optimisethe modulation order based on the EE metric, enabling thesystem to sleep and turn off the circuitry. This optimisationprocedure, combined with MIMO availability, outperformsSISO systems in almost any transmission distance, at thecost of increased instantaneous transmission power.

Still considering MIMO systems, there is the possi-bility of using a significantly large number of antennasat the BS, which has been named large, dense or mas-sive MIMO. In this topology, hundreds of antennas aredeployed only at the BS, while the MSs are equipped withone or few antennas. As described in [57], deploying amassive number of antennas at the BS makes the MS-BSchannels to become pairwise orthogonal; as a result, MAconfigurations using all the time-frequency resources at thesame time can be implemented with low degradation whenan adequate receiver (uplink) or precoding (downlink) isaggregated; besides, under massive antenna condition, thesmall-scale fading can be averaged out. Based on theseconsiderations and comparing linear receiving filters, theauthors in [57] show that it is possible to achieve a highgain in terms of energy and spectral efficiencies for themulti-user case, mainly when an improved linear receiveris deployed as, for example, the zero-forcing detector.The impact of the detector choice on the EE-SE perfor-mance results maintain some similarity with those obtainedfor interference-limited systems, such as CDMA [30, 35,58]. When considering the low power regime (or low SEregime), it is demonstrated that both EE and SE can bemaximised, despite that this scenario is generally not ofgreat interest.

The EE formulation used in [57] only considers thetransmission power consumption, discarding the effects ofthe power dissipated by the RF/antenna circuits. Even ifthis fixed power is small, when considering hundreds ofantennas, it is likely that the impact of this term becomessignificant. In [59], the circuitry power is considered in theEE formulation of a massive MIMO system. The problemconsists of optimising the transmission power, subcarrierallocation, number of active antennas and data rate poli-cies for the downlink of a single-cell OFDMA system withsubcarrier reuse. It is considered as constraint an outageprobability limit in each subcarrier, which is incorporatedin the data rate equation to simplify the solution. Byusing fractional programming, Dinkelbachs method andLagrangian decomposition, it is demonstrated that allocat-ing the maximum number of antennas does not necessarilymaximise the EE, given the circuitry power consumptioncost of adding an extra antenna.

Table III summarises representative works and resultsanalysed in this section.

4. EE IN COOPERATIVE OFDMA

As already mentioned, cooperative communications haveseveral benefits that can result in higher EE, not onlyin terms of power consumption but also in deploymentcosts, because RSs have less functionalities and processing



Table III. Representative papers for EE in OFDM/OFDMA non-cooperative systems.

Year Paper Contribution

2011 [5] Power/subcarrier allocation for single-cell downlink/uplink case, considering EE fairness with minimum rateconstraint. Suboptimal iterative solutions are presented for both cases;

2011 [47] Framework for energy-efficiency communication systems, with a realistic BS power model and also a trafficmodel that allows to analyse the energy-efficiency algorithms in large-scale areas, such as a country, which hasdifferent user densities and data necessities;

2012 [41] Smart allocation algorithm, which makes a subcarrier rearrangement between users to admit more users, andalso a power/bit reallocation to reduce power consumption;

2012 [42] Suboptimal iterative algorithm that allocates the subcarriers/power in a fixed or optimised order to the userthat provides the higher EE gain in each iteration;

2012 [43] Survey on uplink resource allocation for OFDMA networks, considering centralised/distributed scheduling,multicell scenario and describing open trends;

2012 [44] The scheduler concentrates the MSs RBs in few time slots, in order to make MSs sleep in some slots to savethe power spent in receiving mode;

2012 [4] EE maximisation by allocating subchannels and bits to each user with overall SE and individual data rateconstraints. The EE-SE trade-off is investigated by increasing the rate/SE constraints;

2012 [59] Analysis of very-large MIMO systems for EE maximisation in downlink OFDMA, considering circuit powerconsumption and optimising the number of active antennas;

2013 [48] Show that OFDMA has higher EE than OFDM systems for downlink case, that is, sharing the subcarriers doesnot increase EE, and a bisection method to solve subcarrier/power allocation;

2013 [57] Analysis of very-large MIMO systems for EE and SE in single-carrier systems with linear filter optimisation;

2013 [8] Analysis of CoMP techniques in terms of energy efficiency, with downlink LTE-based scenario and differentschedulers.

power. When considering the deployment of cooperativenetworks in cellular scenarios, there are several aspects toconsider, for example,

(1) RS placement, which includes the placement loca-tion and the number of stations installed;

(2) cooperation protocol: regenerative or non-regenerative;

(3) inband or outband channel operation;(4) which relays cooperate and how to assign them;

Besides these four basic aspects, there are others thatcan be included, as frequency reuse patterns and CoMP,and the associated optimisation variables, such as transmis-sion power, subcarrier (from the OFDMA case) and timeallocation, now including the RS nodes in the optimisa-tion problem. In this way, there are several opportunitiesin the EE maximisation design for cellular cooperativeOFDMA networks. In this section, we discuss the fouraspects mentioned and then present salient techniques thatcan be combined with cooperative networks to providehigher energy-efficient systems.

4.1. Retransmission protocols andoperation modes

Analysing first the retransmission protocols, we point outtwo possibilities: non-regenerative and regenerative pro-tocols. If we consider only the commercial aspect, the

LTE-A standards only consider the regenerative protocolsfor implementation, as some features such as the AMCis only possible at the relays when they are able to fullydecode the received signal and then adapt the modula-tion and coding scheme to the conditions of the nexthop. Because the proposal of this survey is the literatureoverview, we also consider the non-regenerative protocolsas a possible choice.

The two most common protocols are the AF and DF,respectively, a non-regenerative and a regenerative proto-col. The basic approach for both protocols is to process thereceived signal and then retransmitting the information tothe destination node. As pointed out in several papers, thisapproach can result in poor retransmitted information ifthe received signal is highly corrupted, which is in generalcaused by poor SINR. If we consider AF, under low SINR,the amplified signal consists mainly of interference andnoise, while for DF, BER becomes significantly high, andthe retransmitted data do not correspond to the informationsent by source. One possible way to overcome this problemis to adopt a threshold to decide if the RS is able to coop-erate. In this case, the QoS metric that the RS must obey ismapped into SINR threshold #TH, and if the achieved SINRis greater than #TH, the associate RS is able to cooperate;as a result, relaying mode becomes advantageous.

When choosing the relaying protocol, we also define ifthe relay operation is made in time or frequency domain[60]. For the non-regenerative protocols, the operationcan be made in both domains, amplifying the received



signal in the time domain or taking the DFT to access thereceived signal in each subcarrier/subchannel and adap-tively amplifying each one, retransmitting the signal afterthe IDFT computation. From the EE perspective, it isnecessary to investigate if the obtained gain can compen-sate the complexity/energy cost associated to both DFToperations. When considering the regenerative protocols,the only possibility is the frequency domain operation,because the information is modulated in frequency domain.

There are few papers comparing regenerative and non-regenerative protocols for OFDMA from the EE perspec-tive. Looking at the simplest possible case, as described inFigure 2, there are some results. For example, consideringthe existence or absence of return channel, in [61], the EEof AF and selective DF, which is a protocol that only relayswhen the MS-RS link succeeds, is compared. In the pro-posed scenario, there is not a best protocol for all cases,and this choice depends on certain factors, as equipmentdistances and network topology.

The authors in [62] consider a similar case but usingonly AF and selective DF and a circuit power consumptionmodel that reproduces the extra cost of the DF operation.It is proposed the optimisation of the modulation in orderto minimise the energy-per-bit metric in a three-nodesystem, for different node distances. Under this model,the selective DF outperforms AF protocol, which is theopposite result obtained of the first case. This fact showsthat both protocols and their variants must be further inves-tigated in the OFDMA cellular case in order to provideaccurate results, because the topology is not restricted tothe three-node case.

In addition to classical AF and DF, there are other relay-ing protocols that can be used. For example, an extension tothe AF protocol called shorten-and-forward (SF) protocolhas been proposed in [63]. The main concern in SF pro-tocol is that when the signal is transmitted over two hopsand no detection is performed by the RS, the CP can notbe enough to overcome the delay spread inserted by bothhops. In this way, finite response filters are used at the RSto reduce the increased signal length. The SF approach canresult in EE gains because it is possible to use a shorten CP,which reduces the throughput penalty in OFDMA, and canalso be a determinant for multi-hop AF systems, to avoidISI for relayed users.

When considering the separation of backhaul and accesslinks, there are also two modes: inband or outband. In theinband method, the time/frequency resources are dividedby these two links in the time domain, resulting that theexisting infrastructure can be maintained. The main draw-backs are synchronisation issues and possible interferenceif this task was not well solved. On the other hand, the out-band mode deploys a complete separation of the links as,

The return channel is used to indicate or not the necessity of RScooperationIn general, the failure or success of a given link is observed in termsof the received SINR, for a given threshold, which results in the modeldescribed in the second paragraph of Section 4.1

for example, the use of a second carrier frequency for oneof the links. In this case, the synchronisation problem isnot an issue, because both links are separated, at the costof new equipments and, in the case of an extra carrier fre-quency, the cost of this extra frequency band. These twoschemes are depicted in Figure 6, including a third modelthat is proposed in [64] and described in the following.

In [64], the authors investigate the backhaul/accesslinks separation considering capacity enhancement for adual-carrier OFDMA system with DF relays. There arefour proposed carrier allocation modes, two inband andtwo outband, and an interesting result is that both schemesachieve almost the same statistical throughput (in terms ofcumulative density function) and relative gain. This resultcomes from the fact that the allocated resources in each

Figure 6. Schemes for access/backhaul links separation:(a) inband, (b) outband and (c) mixed inband/outband proposed

in [64].



case are almost the same: regarding the inband case, allcarriers are allocated half of the total time, while in the out-band case, only one carrier is available but is allocated allthe time. Despite this equality, it is also pointed out thatsome factors can result in capacity loss for the inband case,given by synchronisation, backhaul/access switching andthe necessity of transmitting control data from BS to RSusing resources that are reserved for user data. A capacityloss of about 8 % 12% is expected. In order to enhanceEE, it is also investigated the resource sharing optimisa-tion, given by dynamically sharing the resources for thebackhaul link. Numerical results show the effectiveness ofthese optimisations, mainly for the worst users.

In [65], the authors consider the downlink of a multicellOFDMA system, in which each cell is aided by some RSs.The proposed relay protocol separates the backhaul andaccess links in time, splitting equally the access time, andthe two-hop and one-hop communication in frequency, byallocating orthogonal subchannels to each mode. The opti-misation problem considers the transmission mode (oneor two hops), subchannel allocation and relay processingdesign, while the BSs transmission power is consideredfixed. The proposed solution consists in trying all the possi-ble modes and subchannel allocations and then evaluatingthe relay processing design, which has been shown noloose in optimality when compared to the optimal solu-tion, which consists of trying all the combinations of thesethree variables. Despite this simplification, the complexityis still prohibitive; hence, these problems can be sequen-tially solved in a single-cell way by a heuristic strategy. Itis shown that the DF protocol results in higher power effi-ciency, measured in bits per Watts, than the two protocolsbased on AF protocol with multiple-relay or single-relayselection strategies.

Besides the pure inband/outband strategies, in [18]and [64], a mixed inband/outband separation model isproposed. As the backhaul link can become the system bot-tleneck, mainly when the number of relays/relayed usersincrease, it is defined that the second carrier is used onlyfor backhaul and direct links, while the first carrier is usedby these two links and the access link, using time divi-sion between backhaul and access links. This alternativeis proposed to increase the efficiency of link usage, as inpure outband systems, if one link is underloaded, the spec-tral resource is wasted, and to prevent backhaul bottleneck,providing extra bandwidth. As pointed out in [64], thismethod outperforms both inband and outband cases.

Another possibility is the deployment of the full-duplexinband relaying, as pointed out in Section 2.2. The full-duplex inband relaying with interference cancellation isconsidered in [66], where full-duplex and half-duplex AFand DF relaying can be dynamically selected in orderto provide a higher average-weighted system throughput.When the self-interference residual term is low to mod-erate, the full-duplex relaying becomes the most selectedstrategy, because it can reduce the number of time slotsnecessary for cooperation: considering that one packetneeds one time slot to be transmitted, transmitting Np pack-ets requires 2Np slots in half-duplex and only Np C 1 slots

in full duplex. When the residual interference becomesmore significant, the numerical results in [66] show thatthe DF half-duplex protocol is more selected that the AFfull-duplex, due to the fact that AF also amplifies the resid-ual interference and noise. In [67], this the same schemeis proposed for MIMO-OFDMA systems, and it is shownthat the full-duplex mode is preferred when the numberof antennas is higher and the residual interference is lowor medium, given its higher SE, while higher interferencereduces the gain obtained with full-duplex, and then thehalf-duplex method is the most likely to be selected. Inboth cases, the AF protocol is marginally adopted, giventhe noise/interference amplification.

The full-duplex inband relaying is also investigated in[68], considering the AF protocol. In the proposed sys-tem model, it is considered that the destination is indoorand the RS has an outdoor antenna to communicate withthe source and an indoor antenna to communicate with thedestination. A direct link between source and destinationis also available, but it is not considered in the numericalevaluation. In addition to the antenna isolation and interfer-ence cancellation, the authors also discuss the impact of AFgain optimisation to reduce the residual interference andimprove SINR, showing through numerical results that itis possible to determine an amplifying gain that maximisesthe received SINR mainly when the residual interferencechannel gain becomes higher. In [69], the authors analysefull-duplex and half-duplex relaying for AF and DF proto-cols and, based on the SINR of the links and the residualinterference, determine the boundaries where each relayingmethod or the direct transmission is more efficient in termsof system capacity. The analysed system topology is ableto switch between direct transmission, half-duplex relayingand full-duplex relaying modes, but the switching betweenAF and DF protocols is not available.

Energy efficiency for direct, half-duplex inband relay-ing and two-way relaying transmissions in single-carriersystems has been compared in [70]. The objective in theproposed analysis is to determine the minimum power toachieve a given data rate between two nodes in both direc-tions, considering asymmetric traffic in each direction andAF protocol. Considering the cost of the interference can-cellation deployed at the receivers for two-way mode, aswell as the cost of detection, it is shown that none of thetransmission modes have higher EE in all scenarios, andthe best mode depends on the path-loss exponents, dis-tance between the nodes and the RS, necessary data rateand the fixed power costs. An interesting conclusion is thatwith higher path-loss exponent and minimum data rate, thetwo-way relaying tends to result in higher EE than directtransmission, which is a result that cannot be achieved withhalf-duplex inband relaying, because the extra time-slotrequirement imposes a double data rate [35]. In metropoli-tan 4G scenarios, the combination of high data rates andsevere attenuation can result in an interesting scenario fordeploying two-way relaying.

Under multicarrier scenario, Sun et al. [29] discussesan energy-efficient approach for OFDM systems, where a



hybrid method combining half-duplex inband and two-wayrelaying modes has been proposed. Considering delay-sensitive users, it is specified the amount of bits that mustbe transmitted at the two time slots, and then the prob-lem is modified to a power minimisation problem. In thishybrid model, the two-way and half-duplex relaying modesdeploy disjoint subsets of subcarriers, because one of theequipments does not need to transmit in that subcarriers,that is, the number of used time slots in this hybrid mode isalways equal to two. The optimisation problem considersthat each equipment has a circuit power and a subcarrierprocessing power, where the last one depends on the num-ber of active subcarriers. Hence, there exists a trade-offbetween transmission power minimisation and total powerminimisation, which leads to a reduction in the numberof allocated subcarriers. The numerical results show that aconsiderable gain can be achieved in terms of EE with thishybrid relaying method, and, again, that the EE for two-way relaying is higher than the half-duplex inband relayingin the high SE region.

Basically, the only decision to make in outband mode iswhich link to allocate in each available carrier. The com-mon approach is to define that one carrier supports thedirect/access links, and the second is used only for thebackhaul link, as they can limit system performance. Onthe other hand, the inband mode has some parameters tochoose as, for example, the backhaul/access links propor-tion, as discussed in [71] and [72]. In [72], it is analysed thedynamic time allocation for backhaul and access links andresource reuse for the downlink of a single-cell OFDMAmacro-cell aided by RSs aiming to maximise capacityunder fairness constraint. In the proposed method, MSsfrom different RSs are able to share the subchannels, thatis, spatial reuse of resources is permitted only in the accesslink. Numerical results show the achievable gains with thecombination of these techniques, which can be maximisedif relay placement optimisation is also considered. In [73],the time sharing is also mentioned, but no numerical resultsare provided. When considering the outband mode with anexclusive carrier to the backhaul link, one possible problemarises when a few number of users deploys RSs, resultingin resource wasting. In order to avoid this situation, somedirect users can be allocated to the backhaul link if it isunderloaded [64].

In [71], relaying techniques based in bandwidth sharingand time sharing are discussed, initially assuming caseswith only one relay, and then a problem with multiplerelays is described, but only the single-relay case is con-sidered. A salient result is that the time-sharing system canbe equivalent to the bandwidth sharing if average powermetrics are considered, while if peak power constraintsare considered, the performance can be worse in terms ofpower minimisation. All the proposed problems includeequipment prioritisation, which turns possible to increasethe power cost of specific equipments as, for example, theMSs, which have limited batteries. The case of multiplerelays is simulated, and a flexible bandwidth sharing model

is proposed to allocate resources for BS-RS links, which isshown to reduce power consumption over fixed strategies.

The EE achieved with the cooperation schemes ofFigure 2(b) and (c), named, two-hop half-duplex andmulticast cooperative scheme, respectively, has been inves-tigated in [74]. Considering the downlink of an OFDMAsystem and optimised resource sharing between theaccess/backhaul links, the numerical results show that thetwo-hop protocol results in higher EE, while the multicaststrategy maximises the average data rate. Another resultcomes from [75], in which the strategies from Figure 2(a)and (b) are compared in terms of EE maximisation; numer-ical results corroborate the superiority of the later schemein terms of EE.

Table IV summarises representative works and resultsanalysed in this section.

4.2. RS deployment

The distance from the RSs to the BS, the number of RSsand even the distribution of the RSs in the cell couldresult in different EE gains. When the RSs are close tothe BS, more users are assisted by relays, but the back-haul link could be saturated, and when the RSs are nextto cell edge, few users can exploit the benefit of relaying.With few RSs, the MS-RS distance can be higher, whichresults in higher power consumption to compensate pathloss. Increasing this number results in higher probability offinding a better RS, but again, the backhaul link may limitthe EE gain. Finally, if the distribution of RSs is consideredfor practical scenarios, taking into account, for instance,identifiable geographical coverage holes and zones withincreased number of users, tends to obtain increased EEthan considering stochastic placement approach. The prin-cipal models for the RS deployment problem are depictedin Figure 7.

The relay deployment problem can also consider theeconomical cost of the installation, as described in [76].Therein, the cost factor depends on the deployment den-sity of RSs and BS stations, considering system capacitynormalised by cell area and deployment of type-I and type-II relays. In order to solve the cost minimisation problem,all the combinations of type-I and type-II RS and BSdensities that result in the same normalised capacity arefound, and then the point of tangency to linear cost line,which represents equal cost for BS-RS density, is takenas minimum deployment cost point. The RS deploymentis compared with micro-BS deployment, and it is shownthat more RSs are necessary to obtain the same perfor-mance as the micro-BS case, but the costs are lower forRS deployment, implying that RSs are the best choice.Extending this work and only considering type-II relays,the authors in [77] introduce user experience satisfactionmetrics to the problem, indicating that not necessarily thebest-cost deployment results in best experience satisfac-tion for the mobile users. In [78], the authors compare thedeployment of RSs with femtocells deployment, and it isshown that topology with RSs can achieve lower power



Table IV. Representative papers for retransmission protocols and operation modes.

Year Paper Contribution

2009 [68] Analysis of full-duplex relaying for AF-based systems, considering indoor-outdoor communication;

2010 [62] Energy-per-bit minimisation by optimising the modulation order for AF and selective DF deploying ornot MRC for both protocols;

2011 [64] Analysis of inband and outband modes with different combinations of carrier usage, resource sharingoptimisation and proposition of a mixed inband/outband mode;

2011 [74] Demonstrate that the two-hop model (Figure 2(b)) results in higher EE than the multicast two-hopmodel (Figure 2(c)), while for capacity, the opposite occurs;

2011 [66] Analysis of half-duplex and full-duplex relaying for AF and DF protocols, with an algorithm that switchesbetween the duplexing modes and retransmission protocols;

2011 [69] Analysis of half-duplex and full-duplex relaying for AF and DF protocols, determining boundaries toswitch between direct, half-duplex and full-duplex for each relaying protocol;

2011 [70] Comparison of the achievable EE for non-cooperative, half-duplex inband relaying and two-way relayingtransmission modes, with or without detection/interference cancellation energetic costs;

2012 [61] EE for AF and selective DF with network coding and with/without return channel and fairness, and theEE contours, which are the data rates that results in the same EE;

2012 [63] Application of shorten-and-forward to reduce ISI due to the extra delay spread caused by the two-hopcommunication;

2012 [65] Optimisation of subchannel allocation, transmission mode and relay processing to increase powerefficiency, comparing AF and DF protocols for single-relay or multirelay selection;

2012 [71] Bandwidth/time-sharing scheme for type-I relays in LTE-A systems for power minimisation, consider-ing equipment prioritisation and data rate requirements;

2012 [72] Dynamic time allocation for backhaul and access links and resource reuse in the access link fordownlink aiming to maximise capacity under fairness constraint;

2013 [29] Analysis of hybrid half-duplex inband and two-way relaying for OFDM systems.

consumption than femtocell-based deployment whilereducing operational and capital expenditure when com-pared with macro-BS only scenarios.

The simplest approach to a relay-based network is toconsider that the RS is between the BS and all mobile users,so that all users are relayed [79]. For the cellular networkcase, this is not a suitable model, because we can haveusers closer to the BS, which can communicate directlywith it. A more sophisticated approach to the relay place-ment problem is to define that the RSs are placed in acircumference of radius R centred at the BS, where R isthe cell radius and 0 < < 1, where the number of RSsand the parameter are modified aiming to optimise EE.This method is well established in the literature and haseven been used for second-generation systems, as CDMA[80]. In [35], the relay placement optimisation is inves-tigated in the uplink of direct-sequence CDMA systems,with non-regenerative outband RSs and outage probabil-ity constraint. It is shown that even for the most inefficientdetector, the RSs bring significant gains for EE and outageprobability reduction, which combined to filter optimisa-tion at least double the EE normalised by bandwidth, giventhe extra carrier for outband relaying. Furthermore, consid-ering the OFDM/OFDMA case, this BS-centred approachwithout placement optimisation can be seen in [65] and

[81], while in [71] and [72]||, the placement distance and/ornumber of relays are analysed aiming to obtain powersaving and capacity gain, respectively.

The benefits of a well-planed RSs deployment can beconfirmed analysing the results from [82], at least interms of system capacity. The authors consider a multi-cell scenario, where only in the central cell, the relayscan be placed in candidate locations in a ring area cen-tred at the BS, while in the other cells, the RSs arelocated using uniform distribution over a circumferencealso centred at the BS. For comparison purpose, simula-tion results include a second approach, where all cells usethe BS-centred uniform distribution. In this context, twooptimisation problems are proposed: maximise overall sys-tem capacity or maximise the capacity for cell-edge users,which are defined as the users that cannot achieve a targetSINR. Solved using nonlinear programming, it is shown bynumerical results that the optimised deployment of relaysreduce the probability of low data rates over the uniformplacement and non-cooperative scenario.

In [83], an iterative RS placement algorithm for multicellWiMAX systems is proposed, in which the RS that pro-vides the higher capacity gain at each iteration is selected.

||In this work, the relay placement is carried out in a ring area and notin an circumference, but the method is almost the same.



Figure 7. Relay deployment strategies: (a) RSs uniformly dis-tributed over a circumference of radius R centred at BS,(b) RSs placed inside a ring area delimited by R and R and(c) stochastic placement. NRs D 6 in (a) and (b) and NRs D 8

in (c).

Instead of choosing a placement radius, the RSs can beplaced in any candidate position associated to an annularsection of each cell, which begins at the limit of BS cov-erage area, which corresponds to the area where the BS isable to provide sufficient quality for the MSs. Numericalresults confirm the throughput gain when deploying thisiterative RS placement algorithm in the multicell system.

When considering coverage extension, the authors in[84] develop a method to determine the non-transparentRS placement radius in order to achieve a higher cover-age radius. Defining coverage as the area where a specificSINR detection threshold is maintained, the coverage ismaximised in such a way that both the BS-RS and RS-MS links are able to achieve the SINR threshold, and the

number of RSs is determined in order to obtain non-overlapping coverage between two RSs, without optimis-ing this variable, and they are placed in a circumferencecentred at the BS. This approach is evaluated numericallyfor both single-cell and multicell cases, where the first oneis optimally solved and the second one is solved by aniterative suboptimal algorithm.

Instead of deploying RSs for each cell, in [85], theauthors discuss a two-cell case and propose to place justonly one RS in between the two cells. The whole idea con-sists in reducing the ICI and the inter-relay interference,which results from the standard cooperative approach byusing this unique relay for both cells. In the first time slot,each BS transmits to the users, and the relay overhears thistransmission from both cells. In the second time slot, theRS chooses one of the received signals in each subcarrierand then retransmits to all covered users. In this way, eachrelayed user receives its own information or the informa-tion of the other cell user. If it receives its own information,the user can use combination techniques to obtain diversitygain; in the other case, the relayed information can be usedto cancel the ICI, also resulting in SINR gain.

Unlike the hypothetical models considered earlier, themodel described in [73] considers the coverage map of onearea in London and discusses RS deployment in order toprovide better coverage and higher capacity and also com-pares the obtained results with the deployment of micro-BSs. RS and micro-BS equipments can be installed in anylocation of the studied region, and this decision is madeby employing a metric defined by the authors that consid-ers the outage probability, backhaul link quality, coverageand system capacity, which can be weighted to providedifferent objectives. As the micro-BSs have a dedicatedlink with the BS, the backhaul link bottleneck does notaffect the result, and no access/backhaul resource sharingis necessary, which results in higher coverage and capac-ity gain when compared with RS deployment. Hence, onecan identify a possible trade-off between cost efficiencyand coverage/capacity enhancement when comparing [76]and [73].

A different metric from all the aforementioned citedworks is known as stochastic geometry (SG). Used inseveral resource allocation problems and networks per-formance analysis, the SG approach is employed, forinstance, in the RS placement problem [86]. As the place-ment problem is considered under a stochastic perspective,the placement distance and geometry are also stochastic,allowing us to analyse the effects of RS and BS densi-ties on the systems EE. The stochastic approach does notseem to reflect commercial deployment situations, but theauthors remember that the existent deployments found inpractical scenarios are highly non-regular, which resultsthat SG approach is an adequate framework for cellularnetwork efficiency evaluation. Another advantage of thesolution in [86] is that the EE is determined by the expec-tation of the stochastic definition of the problem, resultingin an analytical solution, at the cost of considering fixedpower allocation.



Relevant works and results for relay deployment opti-misation problem compiled in this section are listed inTable V.

4.3. Relay assignment

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