Energy-Efficient Cooperative Techniques for Infrastructure-to-Vehicle Communications

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 12, NO. 3, SEPTEMBER 2011 659

Energy-Efficient Cooperative Techniques forInfrastructure-to-Vehicle Communications

Tuan-Duc Nguyen, Olivier Berder, and Olivier Sentieys, Member, IEEE

Abstract—In wireless distributed networks, cooperative relayand cooperative multiple-input–multiple-output (MIMO) tech-niques can be used to exploit the spatial and temporal diversitygains to increase the performance or reduce the transmissionenergy consumption. The energy efficiency of cooperative MIMOand relay techniques is then very useful for the infrastructure-to-vehicle (I2V) and infrastructure-to-infrastructure (I2I) commu-nications in intelligent transport system (ITS) networks, wherethe energy consumption of wireless nodes embedded on roadinfrastructure is constrained. In this paper, applications of co-operation between nodes to ITS networks are proposed, and theperformance and the energy consumption of cooperative relayand cooperative MIMO are investigated and compared with thetraditional multihop technique. The comparison between thesecooperative techniques helps us choose the optimal cooperativestrategy in terms of energy consumption for energy-constrainedroad infrastructure networks in ITS applications.

Index Terms—Cooperative multiple-input–multiple-output(MIMO), distributed space-time coding, energy efficiency,infrastructure-to-vehicle communications, wirelesscommunications.

I. INTRODUCTION

IN future intelligent transport systems (ITS), information andcommunication from the road infrastructure to vehicle (I2V)

will play a key role in driving assistance, floating car data, andtraffic management to make the road safer and more intelligent.The communications are supported by wireless nodes that areintegrated in road signs (or traffic infrastructure along the road)and vehicles. Although wireless nodes that are embedded invehicles can take profit from their battery or can regularly berecharged, each road sign wireless node is usually powered bya small battery that may not be rechargeable or renewable fora long time (or powered by a low power solar battery). Evenif such networks are mainly concentrated in cities (but newapplications also appear for rural junctions), many of the nodesare not necessarily connected to an electrical power supplydue to the civil engineering cost. The energy consumption ofroad infrastructure wireless nodes is, consequently, one of the

Manuscript received August 15, 2009; revised September 13, 2010; acceptedFebruary 7, 2011. Date of publication March 17, 2011; date of current versionSeptember 6, 2011. The Associate Editor for this paper was S. Ukkusuri.

T.-D. Nguyen is with the School of Electrical Engineering, Ho Chi MinhCity International University, Vietnam National University, Ho Chi Minh City70000, Vietnam (e-mail: [email protected]).

O. Berder and O. Sentieys are with the Institut de Recherche en Informatiqueet Systèmes Aléatoires (IRISA), University of Rennes 1, 35042 Rennes Cedex,France (e-mail: [email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TITS.2011.2118754

important constraints when increasing the reliability and thelifetime of this network.

As the transmission power quickly increases as a K powerfunction of the transmission distance (with typical path lossfactor 2 < K < 6), the transmission energy consumption playsan important role for medium- and long-range transmissionand represents the dominant part of the total energy con-sumption. In some ITS applications, energy-efficient trans-mission techniques are very important for the communicationfrom an energy-constrained device such as road I2V or toanother energy-constrained device [road infrastructure to roadinfrastructure (I2I)]. In the traditional approach, the multihoptransmission technique is used to reduce the transmission en-ergy consumption by dividing the long transmission channelinto multiple short transmissions.

The cooperative relay technique can exploit the spatial andtemporal diversity gains to reduce the path loss effect in wire-less channels. The result is that the system performance isimproved or less energy is needed for data transmission. Relaytechniques are recognized as a simple energy-efficient way ofextending the transmission range due to their simplicity andtheir performance for wireless transmissions over fading chan-nels [1]–[3]. These techniques have recently been studied in thecontext of vehicle-to-vehicle (V2V) communications in [4].

Aside from the relay technique, some individual sensornodes can cooperate at the transmission and the reception todeploy a cooperative multiple-input–multiple-output (MIMO)transmission scheme [5]–[7]. Classical MIMO transmission isinvestigated for V2V transmissions and should be proposed inthe future IEEE 802.11.p standard. Unfortunately, nodes thatare embedded in the road signs cannot have more than oneantenna because of the limitations in space, cost, and energyconsumption. Therefore, classical MIMO cannot be applied toI2I and I2V communications. On the other hand, cooperativeMIMO can exploit the diversity gain of the space–time codingtechnique to increase the system performance or to reduce theenergy consumption. In [8] and [9], it has been shown thatcooperative multiple-input–single-output (MISO) and MIMOsystems are more energy efficient than single-input–single-output (SISO) and traditional multihop SISO systems formedium- and long-range transmission in wireless distributedsensor networks. Other recent works on MIMO space–timeblock code (STBC) transmission in ITS applications can befound in [10] and [11]. One the other hand, cooperation be-tween nodes can also help extend the transmission range (withthe same output power of one wireless node), thus increasingthe communication distance between two nodes or two groupsof nodes.

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660 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 12, NO. 3, SEPTEMBER 2011

Fig. 1. I2I and I2V wireless communications in the CAPTIV Project.

In this paper, these cooperative techniques are adopted toITS applications and characterized for I2V and I2I cooperativetransmissions. The context of this paper is the CooperativeStrategies for Low-Power Wireless Transmissions BetweenInfrastructure and Vehicles (CAPTIV) Project [12], where anetwork composed of wireless nodes at a junction has to givearriving vehicles short-term information for driving assistanceand long-term information for traffic management. It is shownthat the cooperative MIMO and relay techniques are better thanthe SISO and multihop SISO techniques in terms of perfor-mance and energy consumption. Both techniques are interestingin the energy-constrained ITS applications, and the advantagesof each technique depend on the particular network structureor on the application. Based on a reference model, energy con-sumption calculations help us choose the optimal cooperativestrategy in terms of energy consumption for CAPTIV withrespect to the transmission distances between two junctions orbetween a junction and a vehicle.

The rest of this paper is organized as follows. The principle ofcooperative strategies for the energy consumption optimizationare presented in Section II. In Section III, the energy calculationmodel is proposed, and simulation results on the energy con-sumption comparison of cooperative techniques in CAPTIV arepresented. Finally, conclusions and discussions are contained inSection IV.

II. COOPERATIVE TRANSMISSIONS AND COOPERATIVE

STRATEGIES FOR LOW-POWER WIRELESS TRANSMISSIONS

BETWEEN INFRASTRUCTURE AND VEHICLES CONTEXT

A scientific coordination group devoted to intelligent trans-portation systems (ITSs), called Groupement d’Intérêt Scien-tifique (GIS) ITS Bretagne, has been set up in the Brittanyregion of France to investigate this research area. One of itsprojects, i.e., CAPTIV, aims at using existing infrastructure,i.e., not only road signs but also every infrastructure alongthe road, to transmit information inside a wireless network,including equipped vehicles, as illustrated in Fig. 1. The firstapplications offered by CAPTIV are road signs anticipated dis-plays (including dynamic situations as temporary works on the

Fig. 2. Three-terminal relay diversity scheme.

road) and arriving vehicle indications (e.g., to help a driver at astop whether to start on the main road in case of smog, heavyrain, or snow). In such a network, every kind of informationcan be transmitted, leading to more advanced applications thatintegrate live data and feedback from a number of other sources,e.g., parking guidance and information systems, and weatherinformation.

In the CAPTIV system, information is transmitted due tovehicles and existing infrastructure within a network whosetypical size is metropolitan. The communications can occurfrom I2V, I2I, a vehicle to road infrastructure (V2I), or fromone vehicle to another vehicle (V2V). The energy constraint forroad sign infrastructure is very important, because batteries intraffic road signs cannot be replaced for a long time.

A. Relay and Cooperative MIMO Techniques

The traditional model for the relay diversity technique withone relay node, as shown in Fig. 2, consists of a sourcenode S, a destination node D, and a relay node R. The relaytransmission from S to D can be performed by a two-time slottransmission. In the first time slot, signals are transmitted by thesource S to the destination node D and the relay node R at thesame time. In the second time slot, the relay node retransmitsthe information previously received. At node D, the receivercombines received signals by using a diversity combinationtechnique, e.g., maximum-ratio combination (MRC) or equal-gain combination (EGC), before symbol detection.

In relay cooperative networks, the received signal comesfrom different independent fading channels so that the

NGUYEN et al.: ENERGY-EFFICIENT COOPERATIVE TECHNIQUES FOR I2V COMMUNICATIONS 661

Fig. 3. Cooperative MIMO transmission scheme from S to D with N coop-erative transmission nodes (S, CT,1, CT,2, . . . , CT,N−1) and M cooperativereception nodes (D, CR,1, CR,2, . . . , CR,M−1).

probability of deep fading is minimized. This diversity gainhelps decrease the error rate or the transmission power for thesame required error rate. Relay techniques can be classifiedaccording to their forwarding strategy. There are three mainmethods for the relay node to transmit the received frame tothe destination node: 1) amplify and forward; 2) decode andforward; and 3) re-encode and forward.

The MIMO technique can exploit the diversity gain of thespace–time coding technique to increase the system perfor-mance or to reduce the transmission consumption for the samebit-error-rate (BER) requirement. The principle of cooperativeMIMO transmission using STBCs was presented in [8]. Asillustrated in Fig. 3, the cooperative MIMO transmission (withN cooperative transmissions and M cooperative receptionnodes) from source node S to destination node D over a trans-mission distance d is composed of the following three phases:1) local data exchange; 2) cooperative MIMO transmission; and3) cooperative reception.

In the local data exchange at the transmission side, the sourcenode S must cooperate with its neighbors and exchange its datato perform a MIMO transmission in the next phase. Node S canbroadcast the transmission bits to the other N − 1 cooperativetransmission nodes. The distance between cooperating nodesdm is usually much smaller than the transmission distance d.In the cooperative MIMO transmission phase, after N − 1neighbor nodes have received the data from source node S,N cooperative transmission nodes will modulate and encodetheir received bits to the quaternary phase-shift keying (QPSK)STBC symbols and then simultaneously transmit to the desti-nation node (or multidestination nodes) similar to traditionalMIMO systems (each cooperative node plays the role of oneantenna of the MIMO system). Finally, in the cooperativereception phase at the reception side, cooperative neighbornodes of destination node D receive the MIMO modulatedsymbols and then sequentially retransmit them to destinationnode D for joint MIMO signal combination and data decoding.In a cooperative MIMO system, the decoder at destinationnode D requires the analog value of received signals at allcooperative nodes for the space–time combination. Therefore,each cooperative node must transmit its received value througha wireless channel to destination node D. One of the followingthree cooperative reception techniques can be used for thisretransmission procedure: 1) quantization; 2) combine and for-ward; or 3) forward and combine [13].

Fig. 4. FER of the relay technique versus the cooperative MISO techniquewith two transmission nodes, noncoded QPSK modulation over a Rayleighchannel, 120 b/frame, source–relay distance d1 = d/3, and power path lossfactor K = 2.

B. Performance Comparison of Cooperative Techniques

Because the cooperative relay and cooperative MIMO tech-nique can exploit the diversity gain to increase the performance,the performance of both techniques is much better than theSISO technique, and the signal-to-noise ratio (SNR) needed issmaller for the same BER requirement. Fig. 4 represents theframe-error-rate (FER) performance comparison of the relay(decode-and-forward and amplify-and-forward techniques) andthe cooperative MISO techniques for two transmit nodes withthe traditional SISO technique.

Because the SNRs of the cooperative MISO and relay tech-niques are smaller than the SISO technique, the two coop-erative techniques can help reduce the transmission energyconsumption for the same transmission reliability in an energy-constrained traffic-signs wireless network. This energy effi-ciency of cooperative MIMO and relay techniques is veryuseful for a typical medium- to long-distance transmission inITS application, where the transmission energy consumptiondominates the total consumption of a wireless node.

The nature of STBCs [14], [15] considers that signals fromdifferent transmit antennas must synchronously be received ateach cooperative node to perform the orthogonal combination.Furthermore, the clock of each wireless node can be driftedduring transmission times, and the transmission delay can varyfor each MIMO channel. Consequently, it is impossible to havea perfectly synchronized transmission in distributed wirelessnodes, leading to an unsynchronized received signal at thereception node. The effect of the transmission synchronizationerror is the superposition of the signal pulses from each node,shifted by the corresponding time delay, at the receiver. Afterthe synchronization and the signal sampling, intersymbol inter-ference (ISI) between the unsynchronized sequences appears,and the space–time sequences from the different nodes areno longer orthogonal. The orthogonal combination of STBCscannot be performed, which leads to the amplitude decrease ofthe desired signal and generates more interferences in the finalestimated symbols [16].

The effect of transmission synchronization in the perfor-mance of the cooperative MIMO technique for the case of


Fig. 5. Effect of the transmission synchronization error on the performance ofthe cooperative MISO systems with two transmit nodes N = 2 and AlamoutiSTBC over a Rayleigh fading channel.

two transmit nodes is presented in Fig. 5. The performancedegradation increases with the transmission synchronizationerror range. The cooperative MIMO system is rather tolerantfor a small range of transmission synchronization errors, andthe degradation is negligible for a synchronization error rangeas small as 0.25Ts (and small for an error range as small as0.5Ts). For a small transmission synchronization error range,the performance degradation is small enough to keep the en-ergy efficiency advantage of the cooperative MIMO systemover the SISO and multihop SISO techniques. However, theperformance degradation is significant for transmission syn-chronization errors as large as 0.75Ts. In this case, a more com-plex distributed STBC or an efficient space–time combinationtechnique can be used to retain the performance of cooperativeMIMO in the presence of a transmission synchronization error.

C. Cooperative Transmission Schemes in the CAPTIV Project

In several communication scenarios in ITS, the transmissionbetween the infrastructure and the vehicles is usually from amedium to long distance, and a direct transmission, if possi-ble, would need too much transmission energy. A traditionalmultihop routing technique can be used for such transmissions,but it is not efficient enough in terms of energy consumption inseveral cases. By exploiting the diversity transmission to reducethe transmission energy consumption, the relay and cooperativeMIMO techniques are the better strategies in terms of energyefficiency.

Considering that the circle and the rectangle stand, respec-tively, for the road sign and the vehicle in the transport system,some cooperative transmission strategies, as illustrated in thefollowing figures, have been proposed for energy efficiencytransmissions in CAPTIV.

1) SISO Multihop Transmission: The most simple cooper-ation scheme is the multihop SISO transmission, as shownin Fig. 6. Instead of the transmission over a long distancefrom source node S to destination node D, a message froma road sign (source node S) at a junction can be transmittedthrough multiple road signs (cooperation nodes) to a vehicle(destination node D). Multihop transmission can significantly

Fig. 6. Multihop SISO transmission between the infrastructure and a vehicle.

Fig. 7. Relay transmission between the infrastructure and a vehicle.

Fig. 8. Cooperative MISO transmission between the infrastructure and avehicle.

save the transmission energy consumption with the cost of morecircuit energy consumption.

2) Relay Transmission: In Fig. 7, a message from the roadsign can be transmitted to the vehicle (destination node D) andanother road sign (relay node R). Then, the message is relayedfrom this relay road sign to the vehicle for signal combination.The transmission diversity gain of the relay technique helpsdecrease the transmission power for the same error rate require-ment so that it reduces the transmission energy consumption.This technique is more energy efficient than multihop SISO formedium-range transmissions.

3) Cooperative MIMO Transmission: The cooperativeMIMO technique is an energy-efficient cooperative techniquefor medium- and long-range transmissions [9]. The cooperativeMIMO technique exploits the diversity gain of the MIMOspace–time coding technique in distributed wireless networksto reduce the transmission energy consumption. Depending onthe system topology (the available nodes) and the transmissiondistance, the optimal selection of transmit and receivenodes number can be chosen to minimize the total energyconsumption.

As illustrated in Fig. 8, a road sign node S can cooperatewith its neighbor road signs to employ a cooperative MISO


Fig. 9. Cooperative MIMO transmission between the infrastructure and avehicle.

Fig. 10. Cooperative MIMO transmission between one infrastructure andanother infrastructure.

Fig. 11. Multihop cooperative MIMO transmission between the infrastructureand a vehicle.

technique to transmit a message to the vehicle (destinationnode D).

As shown in Fig. 9, the road sign node S and the vehiclenode D can cooperate with their respective neighbor roadsigns to employ a cooperative MIMO transmission over along distance. Because the vehicles do not have the surfaceand energy consumption constraints, multiple antennas caneasily be integrated in a vehicle to deploy the cooperativeMIMO schemes without the need of the cooperative receptionphase [9].

Another example of cooperative MIMO transmission inCAPTIV is shown in Fig. 10, where the road sign node S cancooperate with other road signs in one junction to transmitthe message by using a cooperative MIMO technique to thecooperative reception road signs in the other junction.

4) Multihop Cooperative MIMO Transmission: For a long-distance communication, the cooperative MIMO techniquewith the number of transmit and receive nodes greater than 2has energy consumption advantages [9], but this scenario can-not always be employed because of the lack of available nodesat the junctions. In this condition, a multihop technique usingcooperative MIMO for each transmission hop is a suitablesolution. As an example, for a communication between twocrossroads with a distance greater than 1 km in Fig. 11, two roadsigns in the middle of the transmission line can be employed

Fig. 12. Transmitter and receiver blocks with N transmit and M receiveantennas.

TABLE ISNR REQUIREMENT OF THE COOPERATIVE MIMO TECHNIQUE FOR

FER = 10−3 REQUIREMENT AND A RAYLEIGH FADING CHANNEL

(and cooperate together) to perform a multihop cooperativeMIMO transmission.

III. ENERGY EFFICIENCY OF COOPERATIVE STRATEGIES

A. Energy Consumption Model

For a traditional MIMO system (noncooperative MIMOsystem) with N transmit and M receive antennas (N transmitantennas and M receive antennas are integrated into one trans-mitter and one receiver), the typical radio frequency (RF) sys-tem block of transmitters and receivers is shown in Fig. 12. Thetotal power consumption of a typical MIMO system consists ofthe following two components: 1) the transmission power Ppa

of the power amplifier and 2) the circuit power Pc of all RFcircuit blocks.

Ppa depends on the output transmission power Pout. If thechannel is a square-law path loss (power loss factor K = 2),the transmission power needed can be calculated as

Pout(d) = EbRb ×(4πd)2

GtGrλ2MlNf (1)

where Eb is the required mean energy per bit for ensuring agiven error rate requirement, Rb is the bit rate, and d is thetransmission distance. Gt and Gr are the transmission andreception antenna gains, respectively, λ is the carrier wavelength, Ml is the link margin, and Nf is the noise figurereceiver, which is defined as Nf = Mn/N0, where N0 is thesingle-side thermal noise power spectral density (PSD), andMn is the PSD of the total effective noise at receiver input.

Depending on the number of transmit and receive antennas(N and M ) and the PSD of thermal noise N0, Eb can becalculated based on the SNR value as given in Table I for theFER requirement FER = 10−3 and the performance result inFig. 4.

The power consumption Ppa can be approximated as

Ppa = (1 + α)Pout (2)


where α = (ξ/η) − 1, with η being the drain efficiency ofthe RF power amplifier and ξ being the peak-to-average ratio(PAR), which depends on the modulation scheme and theassociated constellation size. Indeed, the power consumption ofthe amplifier is always higher than the effective output power.

The total circuit power consumption of N transmit and Mreceive antennas is given by

Pc ≈ N(PDAC + Pmix + Pfilt + Psyn)

+ M(PLNA + Pmix + PIFA + Pfilr + PADC + Psyn) (3)

where PDAC , Pmix, PLNA, PIFA, Pfilt, Pfilr, PADC , andPsyn stand, respectively, for the power consumption valuesof the digital-to-analog converter, the mixer, the low-noiseamplifier, the intermediate-frequency amplifier, the active filterat the transmitter and the receiver, the analog-to-digital con-verter, and the frequency synthesizer. The power consumptionof signal processing blocks in the transmitter and the receiver istypically much smaller than the consumption of RF blocks. It isconsidered omitted in this estimation for simplicity.

The energy consumption of the traditional MIMO systemEMIMO can be obtained as

EMIMO = (Ppa + Pc)Nb

Rb. (4)

The energy consumption of the SISO technique or one hopof the SISO technique is the case that N = M = 1. The energyconsumption of one transmission phase (from nodes S to R andfrom nodes R to D) of the relay technique can be calculatedsimilar to the SISO technique case.

For a cooperative MIMO system with N transmit and Mreceive nodes, there are three communication phases: 1) thedata exchange phase; 2) the MIMO transmission phase; and3) the cooperative reception phase. The energy consumptionof the MIMO transmission phase can be calculated similarto the noncooperative MIMO case. The total energy con-sumption must include the energy consumption of cooperativedata exchanges and cooperative reception phases. The extracooperative energy consumption at the transmission EcoopTx

and reception EcoopRx sides can be calculated based on thenoncooperative energy consumption model [9].

The total energy consumption of a cooperative MIMO sys-tem with N transmit and M receive nodes is

Etotal = EcoopTx+ EMIMO + EcoopRx

. (5)

For the case of cooperative MISO transmission (M = 1),there are only two first-communication phases, which meansthat the energy consumption of the reception phase EcoopRx

iszero.

B. Energy Consumption Comparison

For energy consumption estimation, evaluation, and com-parison, the reference energy model in [17] with the systemparameters in Table II is used in this paper. More details onthe energy consumption calculation using this reference modelcan be consulted in [9]. Figs. 6–11 represent the total energy

TABLE IISYSTEM PARAMETERS FOR THE ENERGY CONSUMPTION EVALUATION

Fig. 13. Energy consumption of SISO versus the cooperative MISO techniquewith two transmission nodes, power path loss factor K = 2, FER = 10−3, andRayleigh fading channel.

consumption to transmit 107 b with the FER requirementFER = 10−3 from a source node S to a destination node Dseparated by a distance d (over a Rayleigh fading channel). Thelocal distance between cooperative nodes in the cooperativeMIMO techniques is dm = 5 m, and the source–relay distancein the relay techniques is d1 = d/3.

1) Multihop SISO Versus Cooperative MISO Techniques:The energy consumption comparison between multihop SISOand the cooperative MISO is presented in Fig. 13 with theoptimal hop distance dhop = 25 m. At the transmission distanced = 100 m (four hops), the multihop technique can save 53%of the total energy consumption of the SISO system.

The multihop technique is more efficient than the SISOtransmission. However, the multihop SISO system is 69% lessenergy efficient than the cooperative 2–1 MISO system. Atdistance d = 100 m, 85% energy is saved by using the 2–1cooperative MISO strategy instead of SISO. Note that the totalenergy consumption is the consumption of all nodes and notonly one source node. The total energy saving is 69% or 85%for the whole network by using cooperative techniques. Thetransmission energy consumption (which is always greater thanthe reception energy consumption for long distance) is sharedby all cooperative transmission nodes. Moreover, because themultihop system needs four hops for signal transmission tothe destination node, the transmission delay of the multihoptechnique is much more than the cooperative MISO technique,which typically costs two phases of transmission.

Because the performance gain increases with the numberof cooperative transmission nodes in cooperative MIMO tech-niques, the cooperative MISO 3–1 or MISO 4–1 is more


Fig. 14. Energy consumption of the cooperative MISO technique with two,three, and four transmission nodes, power path loss factor K = 2, FER =10−3, and Rayleigh fading channel.

efficient than the cooperative MISO 2–1 or MISO 3–1 at d =180 m or d = 300 m, respectively, as shown in Fig. 14.

If all the RF parameters and the transmission distance arefixed, the transmission energy consumption depends on therequired energy per bit Eb and the power path loss factor ofthe channel [as shown in (1)]. If the FER required increases(less reliable transmission), the required SNR and transmissionenergy consumption will decrease, reducing the energy effi-ciency advantage of the cooperative MIMO over the SISO andmultihop SISO techniques. Otherwise, if the path loss factorK increases (e.g., in an urban environment), the transmissionenergy consumption quickly increases (as a power functionof the path loss factor K). Because the cooperative MIMOtechnique efficiently helps reduce the transmission energy, theadvantage of cooperation increases. As far as the frequencyband is concerned, if the frequency fc = 5.8 GHz (whichwas elected by the European Union for ITS applications andis used in the delicate short-range communication technol-ogy) is considered instead of the reference model frequency2.5 GHz used in this paper, the transmission energy consump-tion increases by (5.8/2.5)K times, and the cooperative MIMOtechnique will probably be more efficient.

Because the nodes are physically separated in a cooperativeMIMO system, their different respective clocks lead to desyn-chronized transmission and reception. This condition generatesISI, decreases the desired signal amplitude at the receiver, andmakes it more difficult to estimate the channel-state information(CSI). At the reception side, each cooperative node has toforward its received signal through the wireless channel to thedestination node for signal combination, which leads to addi-tional noise in the final received signal. The effect of synchro-nization error at the transmission side and this additive noiseat the cooperative reception side lead to some performancedegradations of the cooperative MIMO system [13]. The trans-mission energy needs to be increased for the same error raterequirement, which will lead to an increase in the transmissionenergy and the total energy consumption.

The energy consumption of the cooperative phase (whichdepends on the cooperative distance dm) is much smaller than

Fig. 15. Energy consumption of the cooperative MISO 2–1 with differentcooperative transmission distances dm = 5, 10, and 20 m, FER = 10−3

requirement, and Rayleigh block-fading channel with power path loss factorK = 2.

Fig. 16. Total energy consumption of the cooperative MIMO with differ-ent reception techniques versus the cooperative MISO, ∆Tsyn = 0.25Ts,FER = 10−3 requirement, and Rayleigh fading channel with power path lossfactor K = 2.

the consumption of the MIMO transmission phase for a long-distance transmission (because d � dm). Therefore, the varia-tion of the cooperative transmission distance dm slightly affectsthe total energy consumption of the cooperative MIMO system.Fig. 15 shows the energy consumption of the cooperative MISOsystems with different cooperative transmission distances dm =5, 10, and 20 m.

2) Cooperative MIMO Versus Cooperative MISO Tech-niques: Fig. 16 shows the energy consumption comparisonbetween the cooperative MIMO system with two receive nodesand the cooperative MISO systems 3–1 and 4–1. Forward andcombine, combine and forward cooperative reception (with theamplification factor Kc =

√4) [13], and quantization reception

are used in the cooperative reception phase of the cooperativeMIMO technique, and the transmission synchronization errorrange is considered ∆Ts = 0.25Ts.

The energy consumption of the cooperative MIMO 2–2 usingthe forward-and-combine cooperative reception technique isalways smaller than the cooperative MISO 4–1 consumption


Fig. 17. Optimal N − M transmit and receive antennas set selection as afunction of transmission distance, ∆Tsyn = 0.25Ts, FER = 10−3 require-ment, and Rayleigh fading channel with power path loss factor K = 2.

Fig. 18. Energy consumption of the relay technique versus the cooperativeMIMO technique with two transmission nodes, FER = 10−3, power pathloss factor K = 2, and source–relay distance d1 = d/3.

and smaller than the cooperative MISO 3–1 consumption fordistances d > 130 m. At d = 500 m, there is a 25% energysavings using the cooperative MIMO 2–2 technique instead ofthe cooperative MISO 4–1 technique.

For each range of transmission distance d, based on theenergy calculation result, we can find the best N − M antennaselection strategy of the cooperative MIMO technique in termsof the energy consumption, as shown in Fig. 17. Note that,given the transmission distance and other parameters such asthe quality of service (e.g., FER and the propagation channel),the global energy consumption must be calculated for everypossible N − M configuration of cooperative MIMO by theanalytic formula to perform the selection.

3) Cooperative MISO Versus Relay Techniques: The perfor-mance of the relay techniques is limited by the decoding (orsignal processing) process at the relay nodes. The error bit (oramplification noise) that occurs at the relay node cannot alwaysbe corrected at the destination node. However, with the samediversity gain, the performance of relay is always lower thanMISO space–time coding techniques. Therefore, in many cases,the total energy consumption of the relay technique is higherthan the cooperative MISO technique. Fig. 18 shows the energyconsumption of the relay technique compared with the SISOand cooperative MISO 2–1 techniques.

However, in the presence of transmission errors, the perfor-mance of the cooperative MISO technique decreases, leading tothe increase of transmission energy consumption. The energy

Fig. 19. Energy consumption of the cooperative MISO technique as a functionof transmission synchronization error range, two transmission nodes, error rateFER = 10−3 requirement, and Rayleigh fading channel with the power path-loss factor K = 2.

consumption of the cooperative MISO 2–1 as a function ofthe transmission synchronization error range is illustrated inFig. 19. For a small synchronization error range, the degrada-tion is negligible, but it becomes significant for a large errorrange, leading to a more required transmission energy [13] andless energy efficiency, as illustrated in Fig. 19.

The advantage of the relay technique over the cooperativetechnique is that the relay is not affected by the unsynchronizedtransmission. Fig. 20 shows the energy consumption compari-son of the cooperative 2–1 and relay techniques with the pathloss factor K = 3, and the transmission synchronization errorrange ∆Tsyn is as large as 0.5Ts. In this condition, the relaytechnique is clearly better than the cooperative MISO in termsof energy consumption.

In the case that the number of cooperative transmission nodesN is greater than two (e.g., three or four transmit nodes),the relay technique typically needs N transmission phases totransmit all signals from N − 1 relay nodes to the destinationnode (if orthogonal frequency channels are not considered).However, the cooperative MISO technique typically needs twotransmission phases (data exchange and MISO transmissionphases). The transmission delay of the relay technique is longerthan the cooperative MISO technique. However, the complexityof the relay is less than the cooperative MISO.

IV. CONCLUSION

Cooperative techniques can exploit the transmission diversitygain to increase the performance or reduce the transmissionenergy consumption of the system. Some cooperative strate-gies, which are based on the multihop, cooperative relay, andcooperative MIMO techniques, have been proposed to deployenergy-efficient transmissions between the road infrastructuresand vehicles in CAPTIV.

In this paper, it has been shown that the cooperative MISOand MIMO techniques are more energy efficient than the


Fig. 20. Energy consumption of the relay technique versus the cooperativeMISO technique with two transmission nodes N = 2, power path loss factorK = 3, FER = 10−2, transmission synchronization error range ∆Tsyn =0.5Ts, and source–relay distance d1 = d/3.

SISO and traditional multihop SISO techniques for medium-and long-range transmissions. An optimal cooperative MIMOscheme selection has also been presented to find the opti-mal N − M antenna configuration for a given transmissiondistance.

Cooperative relay techniques provide attractive benefits forwireless distributed systems when the temporal and spatialdiversity can be exploited to reduce the transmission energyconsumption. Relay techniques are more efficient than the SISOtechnique but are still less efficient than the cooperative MISOtechniques in terms of energy consumption. The performanceof the relay techniques is not as good as the cooperative MISOtechniques for the same SNR. However, the relay techniquesare not affected by the unsynchronized transmission scheme.When the transmission synchronization error becomes signifi-cant, the performance of the relay techniques is better than theperformance of the cooperative MISO, leading to better energyefficiency.

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Tuan-Duc Nguyen received the M.Sc. degree fromTelecom ParisTech University, Paris, France, andthe Ph.D. degree from the University of Rennes 1,Rennes, France, in 2005 and 2009, respectively.

In 2009, he was a Postdoctoral Researcher incooperative communications for wireless sensor net-works with the Institut de Recherche en Informatiqueet Systèmes Aléatoires (IRISA) Research Center,University of Rennes 1. Since 2010, he has been aLecturer and Researcher with the School of Elec-trical Engineering, Ho Chi Minh City International

University, Vietnam National University, Ho Chi Minh City, Vietnam. His re-search interests include cooperative communications, wireless sensor networks,and wireless ad hoc networks.

Olivier Berder received the B.S., M.S., and Ph.D.degrees in electrical engineering from the Universityof Bretagne Occidentale, Brest, France, in 1998,1999, and 2002, respectively.

From 2002 to 2004, he was with the Labora-tory for Electronics and Telecommunication Systems(LEST–UMR CNRS 6165), Brest. From October2004 to February 2005, he was with the Speech andSound Technologies and Processes Laboratory, FTR&D, Lannion, Brittany, France. In March 2005,he was with the École Nationale Supérieure des

Sciences Appliquées et de Technologie (ENSSAT)–University of Rennes 1,Rennes, France. He is currently an Assistant Professor with the Institut deRecherche en Informatique et Systèmes Aléatoires (IRISA), University ofRennes 1. His research interests focus on multiantenna systems and cooperativetechniques for mobile communications and wireless sensor networks.


Olivier Sentieys (M’03) received the M.Sc. andPh.D. degrees in electrical engineering (signalprocessing) from the University of Rennes 1,Rennes, France, in 1990 and 1993, respectively.

After completing his Habilitation thesis in 1999,he was with the Graduate School of ElectronicsEngineering of the École Nationale Supérieure desSciences Appliquées et de Technologie (ENSSAT),University of Rennes, as a Full Professor in 2002.He currently leads the CAIRN Research Team withthe Institut National de Recherche en Informatique et

en Automatique (INRIA; French National Institute for Research in ComputerScience and Control) and the Institut de Recherche en Informatique et SystèmesAléatoires (IRISA), University of Rennes 1. His research interests include finitearithmetic effects, low-power and reconfigurable system on chip, the design ofwireless communication systems, and cooperation in mobile systems. He is amember of the editorial board of the Journal of Low Power Electronics. He isthe author or a coauthor of more than 150 journal publications or peer-reviewedconference proceedings and is the holder of five patents.

Prof. Sentieys is the President of the French Chapter of IEEE Circuitsand Systems (CAS) Society and a member of the Association for ComputingMachinery (ACM). He was a Publicity Cochair of the 2010 IEEE InternationalSymposium on Circuits and Systems and has been on several conferenceprogram committees, including the IEEE International Symposium on QualityElectronic Design, the IEEE International Symposium on Design and Diagnos-tics of Electronic Circuits and Systems, the IEEE Vehicular Technology Con-ference, the International Conference on Design and Technology of IntegratedSystems, the Conference on Design of Circuits and Integrated Systems, and theIEEE Northeast Workshop on Circuits and Systems.

Energy-Efficient Cooperative Techniques for Infrastructure-to-Vehicle Communications

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