enabling cooperative relaying vanet clouds over lte-a networks€¦ · computing and connectivity...

1468 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 64, NO. 4, APRIL 2015

Enabling Cooperative Relaying VANET CloudsOver LTE-A Networks

Mohamed F. Feteiha, Member, IEEE, and Hossam S. Hassanein, Senior Member, IEEE

Abstract—This paper addresses the area of heterogeneous wire-less relaying vehicular clouds. We devise an advanced vehicularrelaying technique for enhanced connectivity in densely populatedurban areas. We investigate the performance of a transmissionscheme over a Long-Term Evolution-Advanced (LTE-A) networkwhere vehicles act as relaying cooperating terminals for a down-link session between a base station and an end-user. The abun-dance of moving vehicles, operating in an ad hoc fashion, caneliminate the need for establishing a dedicated relaying infras-tructure. However, the associated wireless links in vehicular cloudsare characterized by a doubly selective fading channel; this causesperformance degradation in terms of increased error probability.Hence, we propose a precoded cooperative transmission techniqueto extract the underlying rich multipath–Doppler-spatial diversity,which is a relay selection scheme to take advantage of the po-tentially large number of available relaying vehicles. We furthercontribute by the derivation of a closed-form error rate expression,diversity gain, and outage expressions and introduce our derivedperformance unconditional expressions as a benchmark to assessour analysis and future research studies of such an approach. Ouranalytical and simulation results indicate that significant diversitygains and reduced error rates are achievable. In addition, there is anoticeable reduction in the required transmitting power comparedwith traditional transmission schemes, as well as an increase indistance coverage.

Index Terms—Best relay selection, cooperative relaying, fourthgeneration (4G), Long-Term Evolution-Advanced (LTE-A), vehic-ular ad hoc network (VANET), vehicular communications.

I. INTRODUCTION

IN cellular networks, inherent limitations on cell capacityand cell coverage exist. Due to the capacity limitation, in

dense urban areas, such as downtown areas and major events,users tend to experience degraded performance. In the searchfor ways to enhance network performance, researchers haveturned their attention to vehicles on the road. The abundantonboard vehicle resources [1] that are underutilized by tradi-tional vehicular applications offer an opportunity for improved

Manuscript received May 18, 2013; revised April 16, 2014; acceptedMay 11, 2014. Date of publication June 10, 2014; date of current versionApril 14, 2015. This research is supported by a grant from the Natural Sciencesand Engineering Research Council of Canada (NSERC). The review of thispaper was coordinated by Dr. J.-C. Chen.

M. F. Feteiha is with the Networks and Distributed Systems Department,Informatics Research Institute, City of Scientific Research and Technolog-ical Applications, Alexandria 21934, Egypt, and also with the School ofComputing, Queen’s University, Kingston, ON K7L 3N6, Canada (e-mail:[email protected]; [email protected]).

H. S. Hassanein is with the School of Computing, Queen’s University,Kingston, ON K7L 3N6, Canada (e-mail: [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/TVT.2014.2329880

computing and connectivity in vehicular ad hoc networks(VANETs) [2]. Indeed, with its diverse resources, includingsensing, processing, storage, and communication modules, avehicle can be a potentially excellent candidate as a “mobilerelay” to support what we call relaying vehicular cloud net-working (RVC-Net). We propose the creation of a “cooperativeRVC-Net” using vehicles equipped with short- and medium-range wireless communication technologies and low elevationantennas. The main reason for using vehicles as relays is toreduce power consumption at the end-user mobile terminal. Byreducing the distance between the transmitter and the receiver,the required transmission power is also reduced. Unlike basestations (BSs), relay stations (RSs) are less complex and do notrequire wired backbone access. In addition, RSs provide higherthroughput, increase coverage, lower operational and capitalexpenditure, allow for faster communication links to roll out,and offer a more flexible configuration. Cooperative RVC-Netcan be an integral part of the next-generation cellular network,namely, Long-Term Evolution-Advanced (LTE-A); LTE-A isthe de facto fourth-generation (4G) wireless system and isexpected to dominate the next generation of wireless networksand to support a wide variety of applications that requirehigher data rates with more reliable transmission. To meet suchdemands, wireless communication system designers need tooptimize network performance in terms of better link reliability,fewer dropped connections, and longer battery life [3]. LTE-Awas ratified by the International Telecommunication Union asan International Mobile Telecommunications-Advanced (IMT-Advanced) 4G technology in November 2010 and has adoptedrelaying for cost-effective throughput enhancement and cover-age extension [4], [5].

The utilization of the proposed cooperative RVC-Net archi-tecture is aimed at increasing network performance without theexpense of expanding network infrastructure. We remark thatuser cooperation has been recently applied to vehicular com-munications to extend coverage, enable ad hoc connectivity,and enhance link reliability through distributed spatial diversity[6]–[12]. To motivate vehicles to act as cooperative relays,incentive mechanisms for multiuser cooperative relaying havebeen proposed in the literature [13].

The main challenge facing the deployment of vehicularrelaying networks manifests itself in the systems advantage,which is the lack of infrastructure. For such types of networks,the physical-layer designs have to cope with tremendous chal-lenges, including an extendable relaying network area overthe entire road with many participants and an extremely dy-namic environment with a topology that is always changing.Furthermore, due to the high speed of moving vehicles, the

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FETEIHA AND HASSANEIN: ENABLING COOPERATIVE RELAYING VANET CLOUDS OVER LTE-A NETWORKS 1469

connectivity of the vehicular relaying could be compromised,changing frequently, which causes a higher probability of dis-connection. As well in LTE-A wireless broadband vehicularrelaying, intersymbol interference (ISI) introduces frequencyselectivity, whereas Doppler spreads result in time selectivity.To realize the full potential of cooperative diversity using ve-hicular relaying networks, it is important to conduct an in-depthinvestigation of performance limits and develop enabling tech-niques to support such broadband transmission. In this paper,we investigate the performance gains of a transmission schemein LTE-A networks where vehicles act as relaying cooperatingterminals for an eNodeB-to-User Equipment (UE) downlinksession. The associated wireless links are characterized by adoubly selective fading channel, which results in performancedegradation, in terms of increased error probability. Hence,we propose a precoded cooperative transmission technique toextract the underlying rich multipath–Doppler–spatial diversity.Furthermore, we implement a best relay selection scheme totake advantage of the potentially large number of availablerelaying vehicles. We further contribute by

1) devising an effective precoding transmission scheme anda relay selection technique that significantly increasediversity gains and reduce error rates;

2) the derivation of closed-form formulas for error rate andoutage probability as a benchmark to assess our analysisand future research studies of such an approach;

3) demonstrating the performance gains of the proposedapproach, analytically and through simulation, comparedwith the traditional approaches.

The remainder of this paper is organized as follows. InSection II, we describe the proposed two-phase dual-hop co-operative system using a best relay selection from M availablevehicles willing to be involved in the transmission session. InSection III, we derive a closed-form expression for the pair-wise error probability (PEP) and demonstrate the achievablediversity gains. In Section IV, we derive the outage probabilityclosed-form expression. In Section V, we present numericalresults to confirm the analytical derivations and provide in-sight into the system performance. Section VI concludes thispaper.

Notations: (.)T , (.)∗, and (.)H denote transpose, conjugate,and Hermitian operations, respectively. E[.], ‖.‖, and ⊗ denoteexpectation, absolute value, and Kronecker product, respec-tively. Bold letters denote the matrices and vectors. [H]k,mrepresents the (k,m)th entry of H. IN indicates an N ×N -size identity matrix. 1 and 0 represent, respectively, all-ones and all-zeros matrix with proper dimensions. �.� and �.�denote integer ceil and integer floor operations, respectively. ∗is the convolution operator. x, i, j, k are dummy variables. F (.)and f(.) are the cumulative distribution function (cdf) and theprobability density function (pdf) for a given random variable,respectively. The distances, geometrical gains, and path-losscoefficient are denoted by d(.), G(.), and α, respectively. L(.)

denotes the number of multipath, and Q(.) denotes the numberof Doppler shifts. The number of available relaying vehicleswilling to contribute in transmission is given by M . We have Pand Z as the precoder design parameters.

II. SYSTEM MODEL

There are two main approaches to handle cooperative com-munications. The first approach involves adaptive transmissionin which one or more transmission parameters (coding, modula-tion, power, etc.) are varied according to the channel conditions.This builds on a closed-loop implementation in which feedbackfrom the receiver to the transmitter is required. The secondapproach is the use of either outer coding or precoding. Theseare open-loop implementations that do not require feedback.Such techniques are particularly useful over time-varying chan-nels where reliable feedback is difficult to obtain. In our paper,considering the time-selective nature of the vehicular systemunder consideration, we used the linear constellation precodingapproach. Such an approach is particularly useful over high-speed mobility communications, where reliable feedback isdifficult to obtain. Taking this into consideration, we will buildour communication scheme over orthogonal transmission pro-tocol, cooperative relaying, as well as outer and linear signalprecoding.

The utilization of multihop relaying techniques is aimedat increasing network performance without the high costs ofexpanding a networks’ infrastructure. The main principle inrelaying results in reduced levels of power consumption forthe mobile terminals. This is the outcome of reducing thetransmitter-to-receiver distance and, hence, the required trans-mission power. Unlike BSs, RSs are less complex and do notrequire wires for the backbone access. In addition, they providehigher throughput, increase coverage, lower operational andcapital expenditure, allow for faster roll out, and offer a moreflexible configuration. An alternate approach for relaying isby making use of vehicles equipped with low elevation an-tennas and short- and medium-range wireless communicationtechnologies.

Adopting such transmission model will directly utilize fiveinherent advantages of vehicular networking.

1) There is abundant energy and computing power (includ-ing both storage and processing).

2) There are predictable movement patterns of vehicles asthey are in most cases limited to roads.

3) Road map information is often available from positioningsystems and map-based technologies such as GPS.

4) The trajectory of a vehicle can be predicted given theaverage speed, current speed, and road map.

5) The frequent availability of traveling vehicles, operatingin an ad hoc fashion, eliminates the need for establishinga dedicated relaying infrastructure.

With its diverse resources, including sensing, processing, stor-age, and communication modules, a vehicle can offer a dis-tributed system that can manage signal processing tasks in afaster and a more efficient way than centralized computing.Although the expectations for this emerging technology are setvery high, many practical aspects still remain unsolved for avast deployment of vehicular networks. A vehicle can offer adistributed system that can manage cooperative relaying tasksin a more efficient way than a roadside dedicated relay bydeploying a precoded transmission that can extract the richunderlying multipath–Doppler diversity. The Doppler diversity


Fig. 1. Cooperative vehicular communication over LTE-A, using M -relaydeployment.

is the outcome of the Doppler shifts resulting from the high-speed vehicle mobility.

We consider a cooperative communication scenario, asshown in Fig. 1, where an eNodeB antenna (source) directlycommunicates with UE (destination) and indirectly througha relaying vehicle (R) that serves as a best selected relayingterminal. All terminals are assumed to be equipped with singletransmit-and-receive antennas and operate in half-duplex mode.We assume the orthogonal cooperation protocol of with decode-and-forward (DF) relaying [14]. In the broadcasting phase, thesource (antenna) transmits its precoded signal to the relayingvehicle (R) and to the destination (UE). In the relaying phase,the relay is engaged in forwarding the received signal only ifit was decoded correctly; otherwise, the relay is silent. Therelay decodes and then forwards a newly decoded copy of theprecoded signal to the destination. The destination makes its de-cision based on the two received signals over the broadcastingand relaying phases.

When the transmitter vehicle and/or the receiver vehicle arein motion, the Doppler phase shift, i.e., the amount of changein the frequency due to the vehicles’ relative mobility, must betaken into account. Ignoring the additive noise, the bandpassreceived signal can be written as [15], [16]

Y(t)=Re

⎧⎨⎩

N (t)∑n=1

αn(t)sl (t− τn(t)) ej(2πfc+ωDn (t))(t−τn(t))

⎫⎬⎭

(1)

or, equivalently, the impulse response as

h(τ, t)=

N (t)∑n=1

αn(t)ej((ωDn (t))(t−τn(t))+2πfcτn(t))δ (τ − τn(t)).

(2)

In the foregoing equations, N (t) is the number of resolvablemultipath components, αn(t) is the attenuation factor of the nthpath, sl(t) is the equivalent low-pass signal, τn(t) is the delayassociated with the nth path, fc is the carrier frequency, and

ωDn(t) is the Doppler phase shift associated with the nth path

due to the mobility and is given by

ωD(t) = 2πfD(t) =2πλv(t) · cos(ϑ) (3)

where ϑ is the signal’s angle to the vehicle, v(t) is the vehicle’svelocity, λ is the wavelength of the carrier, and fD(t) is theDoppler shift. The difference between the Doppler shifts invarious signal components (contributing to a single fadingchannel tap) forms the Doppler spread of the channel and isgiven by fd = 1/Td, where Td is the coherence time of thechannel.

Jakes’ model assumes an isotropic rich scattering around themobile receiver antenna and builds upon a single-ring model.In this model, the angles of arrival ϑ of the waves arriving atthe receiving antenna are uniformly distributed in the intervalof [−π, π). This single-ring model is generally used for cellularsystems that typically involve a stationary BS antenna above therooftop level unobstructed by the local scatterers.

In highly scattered areas, there are a large number of pathsalong with the absence of a dominating line-of-sight path.The central limit theorem suggests that the complex fadingcoefficient can be modeled as zero-mean complex Gaussian.Therefore, the envelope of the channel follows a Rayleighdistribution, whereas the phase is uniformly distributed [17].In vehicle-to-vehicle communication, the antenna is close tothe ground level (1.5–2.5m) and is dynamic with higher speedand variation, which requires considering the local scatteringaround the vehicular antenna. In [18], Akki and Haber con-sidered a scattering model surrounding the mobile terminalswith omnidirectional antennas, assuming two communicatingterminals moving with velocities v1 and v2. For this mobilityscenario, each path will be subject to separate Doppler shifts,yielding to the impulse response [18], i.e.,

h(τ, t) =

N (t)∑n=1

αn(t)ej((ωD1n (t)+ωD2n (t))(t−τn(t))+2πfcτn(t))

× δ (τ − τn(t)) (4)

where ωD1n(t) and ωD2n(t) are the Doppler phase shifts intro-duced by the two mobile vehicles. Here, the delay term can beexpressed as

τn(t) = τi + δτn(t) (5)

where τi is the mean value of multipath time delay, and δτn(t)is the time delay difference for the nth path measured from thatmean value. We can then express (5) as

h(τ, t) =

N (t)∑n=1

αn(t)ej((ωD1n (t)+ωD2n (t))(t−τi)+2πfcτi+φn)

× δ (τ − τi − δτn(t)) . (6)

In (6), φn = (2πfc + ωD1n(t) + ωD2n(t))δτn(t) is uniformlydistributed in [0, 2π). The time correlation function is given by

C(Δt) = σ2J0

(2πλv2Δt

)J0

(2πλv1Δt

)(7)


with t1 and t2 referring to the two different time instants, andwe have Δt = t2 − t1. The zero-order Bessel function is J0,λ is the wavelength of the carrier frequency, and σ2 is thechannel variance. The power spectrum of the complex envelopeis given by

S(f) =σ2

π2fD1m

√ηK

⎡⎣ (1 + η)

2√η

√1 −

(f

(1 + η)fD1m

)2⎤⎦(8)

where η = v2/v1, fD1m is the maximum Doppler shift due tothe motion of the transmitter, and K[.] is the complete ellipticintegral of the first kind. For η = 0 (i.e., v2 = 0), the powerspectrum and time correlation functions reduce back to Jakes’model.

Our aggregate channel model takes into account both small-scale fading and path loss. Path loss is proportional to dα, whereα is the path-loss coefficient and d is the propagation distance.The path loss associated with the distance d from the eNodeBto the UE is modeled as [19]

Ω(d) = 10(128.1−36.7 log10 d)/10. (9)

Let dsd denote the distance from source S to destination D(S → D), with dsri and drid, respectively, denoting, the dis-tances (S → Ri) and (Ri → D), i = 1, 2, . . . ,M . θi is theangle between lines S → Ri and Ri → D. The relative geo-metrical gains are defined as Gsri = (dsd/dsri)

α and Grid =(dsd/drid)

α and can be further defined using the law of cosines

as G−2/αsri +G

−2/αrdi

− 2G−2/αsri G

−2/αrid

cos θi = 1.As for short-term fading, it should be noted that relaying

vehicles R and destination UE have low elevation antennasand are located within a highly scattering urban area. Thisrequires considering the local scattering around transmittersand receivers. The time correlation function is given by

C(Δt) = σ2J0

(2πλveΔt

)J0

(2πλvrΔt

)(10)

where vr and ve are the velocities of the two communicatingterminals. We have a stationary eNodeB antenna (i.e., ve = 0).Assuming a single Rayleigh distribution with a single Dopplershift, the power spectrum and time correlation mathemati-cal functions reduce to having the autocorrelation functionand the power spectrum of the complex envelope. These are,respectively, given by C(τ) = σ2J0(2πvrτ/λ) and S(f) =

σ2(πfDm

√1 − (f/fDm

)2)−1

, with vr being the maximumvelocity of relaying vehicles. In addition to time-selectivityresults from the Doppler shifts, the channel is subject tofrequency selectivity quantified through delay spread τd. Thechannel satisfies the condition 2fdτd < 1 [20]. Note that in thevehicular channel, there are several instantaneous velocities dueto the acceleration/decelerations. The earlier defined maximumDoppler shift fDm

has been therefore calculated based on themaximum velocities experienced.

The block diagram of the proposed cooperative scheme isshown in Fig. 2. The input data blocks s(i) (generated from anM -ary quadratic-amplitude modulation constellation) of length

Fig. 2. Block diagram for (S), (R), and (D). (a) eNodeB (source). (b) Selectedrelaying vehicle. (c) UE (destination).

Nt are divided into shorter subblocks of length Ns (Ns ≤Nt). Let each of these subblocks be denoted by s(n), whichare the input to a linear precoder Θ of size Ns ×Nt. Wehave Ns = PZ and Nt = (P +Q)(Z + L). The number of theresolvable multipath components is given by L = �τd/Ts�, andthe number of Doppler shifts experienced over the data blockis given by Q = �NtTsfd�; we have Ts as the symbol duration.The time-sampled orthogonal frequency-division multiplexingsignal is converted into the frequency domain by implement-ing a discrete Fourier transform (DFT). The DFT renders adiscrete finite sequence of complex coefficients. We choosethe number of coefficients equal to Q+ 1, and the signal isgiven by

[s(n)]q = s(q)e−jwq , q = 0, 1, . . . , Q (11)

where wq = 2π(q −Q/2)/Nt is the finite Fourier bases thatcapture the time variation. From (11), the basis expansionmodel can be used to represent a discrete-time baseband equiv-alent channel for the vehicular doubly selective channel underconsideration and is given by

hB( ; l) =

Q∑q=0

hq(n; l)ej2wq�, l ∈ [0, L] (12)

where hq(n; l) is zero-mean complex Gaussian. Here, denotesthe serial index for the input data symbols. The block indexis given by n = � /Nt�. For the cooperative scheme under


consideration, define H(0)sd,q , H

(0)sr,q , and H

(0)rd,q as the lower

triangular Toeplitz channel matrices with entries given by (12).Let Lsd, Lsr, and Lrd denote the channel multipath lengths forthe S → D, S → R, and R → D links, respectively. Further, letQsd, Qsr, and Qrd denote the number of resolvable Dopplercomponents for corresponding links.

In the broadcasting phase, the received signals at the relaycan be expressed in a matrix form as

ysr(n) =√

GsrEs

Q∑q=0

D(wq)H(0)sr,q(n)u(n) + nsr(n) (13)

where u(n) = Θs(n) is the transmitted data block, Q =max(Qsd,Qsr,Qrd), and Es is the modulated symbol en-ergy. We have D(wq) := diag[1, . . . , exp(jwq(Nt − 1))], andnsr(n)is the S → R additive white Gaussian noise (AWGN)vector with entries of zero mean and N0/2 variance. Usingthe commutativity of products of Toeplitz matrices with vec-tors, we can replace H

(0)sr,q(n)u(n) with U(n)hsr,q(n) and re-

write (13) as

ysr(n) =√

GsrEs

Q∑q=0

D(wq)U(n)hsr,q(n) + nsr(n). (14)

Defining the augmented matrices

hsr(n) =[hTsr,0(n) · · · hT

sr,Q(n)]T

Φ(n) = [D(w0)U(n) · · · D(wQ)U(n)]

we have

ysr(n) =√GsrEsΦ(n)hsr(n) + nsr(n). (15)

Similarly, the received signal at the destination during thebroadcasting phase is given by

ysd(n) =√

EsΦ(n)hsd(n) + nsd(n) (16)

where nsd(n) is the associated S → D AWGN vector withentries of zero mean and N0/2 variance. During the relayingphase, the relay-received signals are fed to the maximum-likelihood (ML) detector given by

argmins

⎧⎨⎩∥∥∥∥∥ysr(n)−

√GsrEs

Q∑q=0

D(wq)H(0)sr,q(n)Θs

∥∥∥∥∥2⎫⎬⎭

(17)

with s as all the possible signal block combinations. We imple-ment “ideal DF” at the relay [21], and the relay then forwards afresh decoded copy of the received precoded signal, i.e., u(n).The received signal during the relaying phase at the destinationis then

yrd(n) =√

GrdEsΦ(n)hrd(n) + nrd(n) (18)

where nrd(n) is the associated R → D AWGN vectorwith entries of zero mean and N0/2 variance. Φ(n) =

Fig. 3. Best relaying vehicle selection.

[D(w0)U(n) · · ·D(wQ)U(n)]. Finally, arranging (16) and(18) in matrix form, we have[ysd(n)yrd(n)

]︸︷︷︸

Y(n)

=√

Es

[Φ(n) 0

0√GrdΦ(n)

]︸︷︷︸

S(n)

[hsd(n)hrd(n)

]︸︷︷︸

h(n)

+

[nsd(n)nrd(n)

]︸︷︷︸

n(n)

. (19)

ML detection is then performed at the destination with thefollowing metric:

argmins

⎧⎨⎩∥∥∥∥∥ysd(n)−

√Es

Q∑q=0

D(wq)H(0)sd,q(n)Θs

∥∥∥∥∥2

+

∥∥∥∥∥yrd(n)−√

GrdEs

Q∑q=0

D(wq)H(0)rd,q(n)

¯u

∥∥∥∥∥2⎫⎬⎭ (20)

with ¯u as all the possible block combinations for the relayedsignal. Please note that the orthogonal receive diversity uses themaximal ratio combiner protocol, which is equivalent to the MLdetection metric [22], [23].

The aforementioned signal model developed for single relaycan be easily extended for a multirelay scenario with relayselection. Let γsd(n), γsr(n), and γrd(n) denote the averageend-to-end SNRs per block for the S → D, S → Ri, and Ri →D links, respectively. As shown in Fig. 3, the best relay will bechosen based on

rsel = argmaxri

{min (γsri , γrid)} (21)

with rsel as the selected relaying vehicle. We have (21) as awell-known “bound” to the SNR best relay selection method[24], [25]. It was proved, i.e., in [27], that the performance met-rics (i.e., diversity gains, bit error rate (BER), PEP, throughput,capacity, etc.) for the cooperative transmission are bounded bythe minimum SNR of the S → R and R → D links, and then,


the selection is done by selecting the maximum of the minimalSNR of the cooperating vehicles’ S → R and R → D links. Thereceived signal matrix has a similar form as in (19).

III. PAIRWISE ERROR PROBABILITY DERIVATION

AND DIVERSITY GAIN ANALYSIS

Here, we investigate the achievable diversity gain of theprecoded cooperative communication using the best relayingvehicle. We assume perfect channel state information (CSI) atthe relay and the destination. For the orthogonal cooperativeprotocol with DF relaying, after removing the block index n formathematical convenience, the PEP at the destination node isgiven by [21]

PRsel

(S→ S

∣∣∣hsd,hsri ,hrid

)≤ PCoopi

(S→ S

∣∣∣hsd,hrid

)+ Psri

(S → S

∣∣∣hsri

)Psd

(S → S

∣∣∣hsd

)(22)

with S representing the erroneously decoded data matrix in-stead of the originally transmitted S. PRsel

(S → S) is the end-to-end PEP, Psri(S → S) is the PEP results from the S → Ri

link, Psd(S → S) is the PEP results from the R → D link, andPCoopi

(S → S) is the PEP results from the cooperative link(i.e., S → Ri and Ri → D, in the case that the relay detects thesignal correctly but the signal resulting from the cooperativelink is detected wrong). The conditional PEP for each individ-ual term in (22) is given by [26]

P (S → S|h) = Q

(√1

2N0d2(S, S|h)

). (23)

Using the lower bounds recently proposed in [28], (23) canbe tightly lower bounded by

P (S → S|h) ≈3∑

m=1

εme

(− ρm

4N0d2(S,S|h)

)(24)

where ε1 = ε2 = 2ε3 = 1/12, ρ1 = 12(√

3 − 1)/π, ρ2 =4(3 −

√3)/π, and ρ3 = 2

√3/π. The Euclidean distance con-

ditioned on the fading channel coefficients is d2(S → S‖h) =hH(S− S)H(S− S)h. Starting with PCoopi

(S → S‖h), (24)can be rewritten as

PCoopi(S → S|h) ≈

3∑m=1

εme

(−

hHsd

χhsd+GridhHrid

χhrid

4 ρmγ

)

(25)

where χ = (Φ− Φ)H(Φ− Φ). Note that the channel autocor-relation matrix is given by Ch,sd := E[hsdh

Hsd], and the chan-

nel rank is ra := rank(Ch,sd) ≤ (Qsd + 1)(Lsd + 1). For theS → D link, we have Qsd = 0 and ra ≤ (Lsd + 1). Using theeigenvalues decomposition of the autocorrelation matrix, wehave Ch,sd = VsdDsdV

Hsd, where Dsd := diag[σ2

0 , σ21 , . . . ,

σ2ra−1] and VsdV

Hsd = Irsd . Let the normalized channel vector

be denoted by hsd of size ra × 1, whose entries are independentand identically distributed (i.i.d.) Gaussian random variables

with zero mean and unit variance. We can replace hsd withVsdD

1/2sd hsd since both will have an identical distribution;

thus, the PEP will remain statistically invariant. Further defineAsd := (VsdD

1/2sd )HχVsdD

1/2sd , where Asd is Hermitian (i.e.,

Asd = AHsd); thus, there exist a unitary matrix Vsd and a

real nonnegative definite matrix Dsd such that VHsdAsdVsd :=

Dsd. The eigenvector of Asd is Dsd := diag[λ0, λ1, . . . ,λra−1]. Since Vsd is unitary, the vector hsd = Vsdhsd willhave a correlation matrix identical to hsd, namely, we haveC := E[hsdh

Hsd] = E[Vsdhsdh

HsdV

Hsd]. From (25), we have

PCoopi(S → S|h)

≈3∑

m=1

εme−

(Grid

hHrid

χhrid

4 +hHsd

Asdhsd4

)ρmγ

. (26)

Following similar steps, we obtain Psd(S → S‖hsd) andPsri(S → S‖hsri), and substituting in (22), we have

PRsel

(S → S

∣∣∣hsd,hsri ,hrid

)

≤3∑

m=1

εme−γsd4 ρmγ

(e−

γsri4 ρmγ + e−

γrid

4 ρmγ)

(27)

where γsd=hHsdχhsd=

∑rsd−1p=0 λp|βsd

p |2, γsri =GsrihHsri

×χhsri = Gsri

∑rsri−1

k=0 αk|βsrik |2, and γrid = Gridh

Hrid

×χhrid = Grid

∑rrid−1

j=0 κj |βridj |2. Define γi = min

i(γsri , γrid)

and γ′i = max

i(γsri , γrid). Note that −∞ ≤ (γi − γ′

i) ≤ 0.

Based on the analysis of the single relay scenario, by selectinga relay that is close to the destination [27], Gsri � Grid andγi � γ′

i. We can rewrite (27) as

PRsel

(S → S

∣∣∣ γsd, γb) ≤3∑

m=1

εme−ρmγsd+γb

4 (28)

where γsd = γγsd is the SNR results from the S → D link.γb = argmax

ri(γi), i.e., γi = γdiγi, is the SNR results from the

relaying link S → Ri → D, where di = min(rsr, rrd). Defineγ = γsd + γb as the total end-to-end SNR. The cdf of γi isgiven by [29]

Fγi(x) = 1 − Psri

(S → S

∣∣∣ γsri > x)

× Prid

(S → S

∣∣∣ γrid > x). (29)

In (29), γsri and γrid are a summation of weighted inde-pendent exponential distributed random variables and followthe hypoexponential distribution, which is also known as thegeneralized Erlang distribution, and will have the pdf’s given in[30] and [31]. The pdf of γi can then be calculated and results inthe form

fγi(x)=

rsri−1∑j1=0

rrid−1∑j2=0

(ci,j1,j2 (αi,j1+κi,j2) e

−(αi,j1+κi,j2)x

)(30)


where

ci,j1,j2 =

⎛⎜⎝rsri−1∏

k �=j1k=0

αi,k

(αi,k − αi,j1)

⎞⎟⎠

⎛⎜⎝rrid−1∏

k �=j2k=0

κi,k

(κi,k − κi,j2)

⎞⎟⎠ .

(31)

The average SNR for the relaying link S → Ri → D isgiven by γi = E[γi]. Recalling the definition of γb = max

i(γi),

we have

Fγb(x) = P

(maxi∈R

(γi) < x

)=

R∏i=1

P (γi < x) (32)

where P (γi < x) is given by

P (γi < x)=

x∫0

fγi(z)dz

=1γi

rsri−1∑j1=0

rrid−1∑j2=0

(ci,j1,j2−ci,j1,j2e

−(αi,j1+κi,j2)x

).

(33)

Noting∑∑

ci,j1 , j2 = 1 [31], from (32) and (33), we obtain

Fγb(x)=

R∏i=1

⎛⎝1γi

− 1γi

rsri−1∑j1=0

rrid−1∑j2=0

(ci,j1,j2e

−(αi,j1+κi,j2)x

)⎞⎠ .

(34)

In the following, we consider two cases: 1) i.i.d. channels and2) independent and nonidentically distributed (i.n.i.d.) chan-nels. For the i.i.d. case, we derive the unconditional PEP byaveraging (28) and using the binomial theorem [32] along withsome mathematical manipulations; note that the relay index ihas been removed for the sake of presentation, and the pdf forthe end-to-end SNR is fγ(x) = fγsd

(x) ∗ fγb(x). The resulting

PEP is given by

Pi(S→ S)≤3∑

m=1

εmA

rsd−1∑p7=0

M−1∑j3=0

(Bj3

rsr−1∑p5=0

rrd−1∑p6=0

G1

)(35)

with

A =M

(γ)M

rsr−1∑j1=0

rrd−1∑j2=0

(αj1 + κj2) (36)

Bj3 =

(M − 1

j3

)(−1)j3

(rsr−1∏p3=0

rrd−1∏p4=0

cj3p3,p4

). (37)

We have Dj3 , p5, p6, p7 and G1, as defined in followingequations:

Dj3,p5,p6,p7

=1(

1 + γ (αp5+ κp6

)− γj3rsr−1∑p1=0

rrd−1∑p2=0

(αp1+ κp2

)

)

×rrid−1∏k �=p7k=0

λk

(λk − λp7)

(38)

G1 = Dj3,p5,p6,p7

×(

4

1 + 4 (αp5+ κp6

) + 4j3rsr−1∑p1=0

rrd−1∑p2=0

(αp1+ κp2

)

− 4ρmγ

4 + ρmλp7γ

). (39)

Likewise, for the i.n.i.d. case, we have Pni(S → S), as shownin the following equation:

Pni(S → S)

≤3∑

m=1

εm

rsd−1∑j3=0

M∑k=1

M−k+1∑p1=1

M−k+2∑p2=p1+1

· · ·M∑

pk=pk−1+1

(−1)k+1

λj3γ

×k∏

i=1

⎛⎝ 1

γi

rsrpi−1∑

j1=0

rrpid−1∑

j2=0

Ai,j1,j2Ti,j1,j2

⎞⎠ (40)

where

Ai,j1,j2,j3 =

(4ρmγ

4 + λj3ρmγ− 4

1 + 4 (αpi,j1 + κpi,j2)

)

Tpi,j1,j2 =ρmγ (αpi,j1 + κpi,j2) ci,j1,j2ρmγ (αpi,j1 + κpi,j2)− 1

.

From (35) and (40), assuming sufficiently high SNR, we findthat the asymptotic diversity gain Dgain,M is given by

Dgain,M = rsd +M (min(rsr, rrd)) . (41)

The diversity gain is a function of the number of relayingvehicles involved in the best relay selection, as well as thechannel order of all the underlying links.

IV. MAXIMUM DISTANCE FOR

MULTIHOP COMMUNICATION

Wireless transmission is constrained by a regulated trans-mission power, which limits the coverage area. We showthat using cooperative transmission can improve the qualityof received signal, in addition to extending the coveragearea. The outage probability Pout is the probability that theerror probability exceeds a specified value γth. Mathemati-cally speaking, Pout =

∫ γth

0 fγ(γ)dγ [33], which is the cdf ofγ, namely, Fγ(γth). By defining our unnormalized aggregatechannel model that takes into account both path loss and small-scale fading, the relative geometrical gains are redefined asGsd = d−α

sd , Gsr = d−αsr , and Grd = d−α

rd . These can be related

to one another through the cosine theorem G−2/αsr +G

−2/αrd −

2G−1/αsr G

−1/αrd cos θ = G

−2/αsd and assuming a normalized


gain for a 1-m distance [34]. Hence, γsd = GsdhHsdχhsd =

Gsd

∑Rsd−1

p=0 λp|βsdp |2. From (40) and the definition of cdf, the

outage probability is given by

Pout,co

=

3∑m=1

εm

M∑k=1

M−k+1∑p1=1

M−k+2∑p2=p1+1

· · ·M∑

pk=pk−1+1

(−1)k+1

ρmγ

k∏i=1

Ψi

(42)

where

Ψi =

Rsrpi−1∑

j1=0

Rrpid−1∑

j2=0

Ti,j1,j2

(ρmγ

(1 − e(−

1ρmγ )γth

)

− 1(αpi,j1 + κpi,j2)

(1 − e−(αpi,j1

+κpi,j2)γth

)). (43)

From (42), dsd can be shown as an effective parameter on theresulting outage probability of our proposed scheme.

V. SIMULATION RESULTS AND DISCUSSION

Here, we present numerical results to demonstrate the errorrate and outage probability performance. LTE-A targets peakdata rates up to 1 Gb/s with up to 100 MHz supported spectrumbandwidth and by making use of high-order multiple antennatransmission. Unless otherwise stated, we consider quaternaryphase-shift keying modulation and assume fc = 2.5 GHz, Ts =500 μs, vr = 60 km/h, α = 3.67, θ = π, Gsr/Grd = −30 dB,and τd = 1.328 μs [5], [35]. We assume that perfect channelstate information is available at the receiving terminals. We usethe precoder Θ with parameters P = 2 and Z = 2. This resultsin [LCoop,QCoop] = [1, 1] for S → R and R → D links, whereQCoop = min(Qsr,Qrd). A frequency–time flat channel isused for the S → D link, i.e., [Lsd,Qsd] = [0, 0]. Our systemand the mathematical model both indicate that the relay isselected somewhere in between the “BS” and the “end-user.”Based on the system parameters stated earlier, our resultanttransmission was ≈ 48 s in duration before the relaying vehi-cle approaches the designated end-user’s exact location. Withapproximately 100–150 Mb/s downlink data rate supported byLTE-A high-mobility technology [5], our scheme is capable oftransferring large data messages. Larger messages/streamingsizes can be divided into data chunks and distributed overseveral cooperative relaying links.

In Fig. 4, we illustrate the PEP expression (40) derived forthe relay selection case under i.n.i.d channels, as compared withthe exact expression (22). Our derived PEP curves are plotted assolid lines, whereas the numerical “exact” PEP curves are plot-ted as dashed lines. The exact PEP can be found by taking theexpectation of the unconditional PEP numerically through therandom generation of h with proper statistics. We assume M =1, 2, and 3 available relaying vehicles with [LCoop,QCoop] =[1, 1]. We observe that the derived PEP provides a tight upperbound on the exact one with about � 0.5 dB difference.For high SNR, the overall system performance improves asthe achievable diversity increases. So far, we derived designscapable of achieving full diversity. Power savings is clearly

Fig. 4. Comparison of the derived PEP in (40) and exact PEP expressions forcooperative vehicular transmission over the LTE-A case with relay selection.

observed; for example, at PEP = 10−5, a transmitting powerconsumption savings of 5 and 7 dB is observed for M = 2and M = 3, respectively, with respect to M = 1. To show theeffect of the mobility on the error performance rates comparedwith stationary relays and the direct noncooperative transmis-sions, we also plot results for these two traditional scenarios.The curves showing direct transmission indicate the traditionaltransmission without the deployment of the cooperative vehic-ular relaying, whereas the curves for vr = 0 km/h and singlerelay M = 1 indicate the traditional cooperative transmissionfor a single stationary relay. Comparing the two traditionalscenarios with our best relaying vehicle selection case and usinga precoded cooperative transmission to extract the underlyingDoppler diversity resulting from the relaying vehicle mobility(vr = 0 km/h and M = 1, 2, or 3), a power consumptionsavings is clearly observed. For example, at PEP = 10−5, atransmitting power consumption saving of 10 and 20 dB isobserved for M = 3 compared with the traditional cooperativerelaying and direct transmission scenarios, respectively. Notethat the precoder extracts the underlying multipath–Dopplerdiversities (L and Q); for the plotted curves, we have L = 1.However, for the stationary cases (vr = 0 km/h), we haveQ = 0, whereas for the mobility scenarios (vr = 60 km/h), wehave Q = 1.

In Fig. 5, we further plot − logP (S → S)/ log(γ) to pre-cisely observe the slope of the PEPs for the best relay selectionscenario, and the achieved asymptotic diversity orders equalto 5, 9, and 13 are achieved. We observe that the resultsconform to those obtained from the derived expression (41),and the full underlying multipath–Doppler–spatial diversity isextracted. In Fig. 6, we investigate the effect of imperfectchannel state information (I-CSI) on the BER of cooperativevehicular relaying schemes through Monte Carlo simulation. Asingle known pilot symbol is periodically inserted in each inputblock s(n). In the I-CSI, we need to estimate the direct S → Dchannel and the indirect S → R → D channel. We consider thestrategy in [36], which is called cascaded channel estimation,


Fig. 5. Diversity order gains for cooperative vehicular transmission over theLTE-A case with relay selection.

Fig. 6. Effect of imperfect channel estimation on the error rate performance.

where the cascaded S → R → D channel is estimated at thedestination terminal, avoiding the need for channel estimationat the relay terminal. We adopt the pilot symbol assisted mod-ulation technique [37], which relies on the insertion of knownpilot symbols in information-bearing data. The known symbolsare periodically inserted into the data sequence. Followingthe well-known minimum mean square estimation (MMSE)technique, a Wiener filter is used to minimize the variance of theestimation error. The corresponding set of the filter coefficientsis optimized for minimizing the estimation error variance. Theoptimum interpolation coefficient matrix of the estimator andthe received pilot vector autocorrelation will then be a functionof the channel autocorrelation matrix. A sliding window cover-ing a total of 11 pilots from preceding and following blocksis used for channel estimation of the fading coefficient. Itis observed that the imperfect channel estimate degrades theperformance for the BER performance, resulting in error floorswhen no relay selection is performed. This observation of theerror floor effect is analytically presented in the Appendix.

Fig. 7. S → D distance in kilometers versus outage probability.

Fig. 8. Effect of changing the S → R and R → D cooperative angle θ.

The main reason is that the asymptotical performance metricsbecome independent of γ and result in the presence of an errorfloor. On the other hand, due to extra spatial diversity achievedby our proposed relay selection scheme, we are able to suppressthe error floor in the simulated SNR ranges.

Fig. 7 shows the coverage extension gains for the opportunis-tic cooperative transmission compared with the direct transmis-sion using the outage probabilities in (42), for γ = 10 dB andγth = 5 dB [38]. For the outage probability of 10−4, coverageextension advantages of� 1.1,� 1.6, and� 1.7 km are observedfor cooperative transmission with M = 1, M = 2, and M = 3,respectively, compared with traditional direct transmission.

The effect of changing the S → R and R → D cooperativeangle θ is plotted in Fig. 8. We assume a number of avail-able relays to cooperate in the transmission M = 3 and θ =(π, 3π/4, π/2, π/4). The results indicate similar diversity gainswith small shifts in the pairwise error curves, indicating tightlyclosed coding gains (the change in the required SNR to achievea similar error rate). The optimum transmission angle is at


TABLE INUMBER OF DOPPLER SHIFTS Q FOR A GIVEN

BLOCK LENGTH AND P−Z PARAMETERS

θ = π, indicating that the eNB/BS, the relaying vehicle, andthe designated end-user equipment are in a straight alignment.

The result confirms our analytical derivations and closed-form expression and shows that based on the basis expansionmodel for the time-variant channels (12), the maximum diver-sity order of time-selective channels depends on the numberof bases. The linearly precoded transmissions maximize theavailable Doppler diversity order and efficiently implementabletransmissions that are designed to enable the maximum achiev-able multipath–Doppler diversity order, which turned out to bethe rank of the underlying channel correlation matrix. In ourstudy, we assume precoder parameters of P = 2 and Z = 2.Hence, the input data block s(n) is of length Ns × 1 (i.e., PZ),and the output of the precoder u(n) is of length Nt × 1 (i.e.,(P +Q)(Z + L)). We then have the precoder output rate equalto Ns/Nt = PZ/((P +Q)(Z + L)). It is clear that increasingthe precoder output rate can be achieved by increasing P and/orZ. This comes at the expense of higher system complexity(mainly received signal detection), as the length of transmit-ted/received blocks is increased. Furthermore, the diversityorder is a function of the channel order (Q+ 1)(L+ 1). Fromthe definition of P and Z, we have Q(1 − (Z + L)Tsfd)−1 < P (Z + L)Tsfd ≤ Q(1 − (Z + L)Tsfd). Hence, we canchoose P and Z to satisfy a minimum required Q, as shownin Table I. The diversity order is then a function of the fadingchannel delay spread, the Doppler shifts (the relative mobility),the symbol duration, and the precoder design (i.e., dimensions).In many communication problems, ML detection reduces tosolving an integer least squares problem, i.e., a search forthe closest integer lattice point to the given vector, and theNt-dimensional vectors will have to span the Nt-dimensionallattice. Applications involving higher speed vehicles and/orthose with large precoder dimensions result in a large searchspace for ML, rendering ML computationally intractable. Insuch cases, sphere detection techniques [39] can be used.

VI. CONCLUSION

We propose an enabling cooperative vehicular relaying trans-mission scheme to contribute toward the formation of an ad-vanced heterogeneous telecommunication network to provideincreased networking capabilities for heavily populated urbanareas. Our transmission scheme makes use of vehicles equippedwith low elevation antennas and short- and medium-range wire-less communication technologies, allowing new functionalitiesand capabilities. To realize the full potential of the proposed

architecture, we conduct an in-depth investigation of perfor-mance limits and gains, as well as development of enablingtechniques to support such broadband transmission. We demon-strate that using vehicles for cooperative relaying in broadbandcellular networks not only potentiates reduced levels of powerconsumption but also provides higher throughput, increasescoverage, lowers operational and capital expenditures, allowsfor faster roll out, and promises more flexibility. Our proposedscheme can be particularly useful in heavily populated urbanareas, where we can take advantage of the potentially largenumber of relaying vehicles to extract extra spatial diversity andassist in the broadband signal transmission.

Our work involves investigating the performance of the pro-posed cooperative vehicular relaying system over the LTE-Adownlink session, in which the eNodeB point communicateswith a UE using cooperating vehicles as relaying terminals,where best relay selection is deployed. For the doubly selective(time- and frequency-selective) vehicular channel under consid-eration, we employ a precoded cooperative transmission tech-nique to extract the underlying rich multipath–Doppler–spatialdiversity. We derive a closed-form expression for the PEP as abenchmark to assess our analysis and future research studiesof such an approach, as well the derivation of the outageprobability expression. We further conduct Monte Carlo sim-ulations against analytical derivations and present the error rateperformance under various mobility scenarios. Analytical andsimulation results demonstrate significant performance gains,where notable diversity gains are achieved, as well as a notablereduction in error rates. Additionally, a reduction in the requiredtransmitting powers compared with traditional transmissionschemes is observed. The coverage distance gains are analyzedusing our outage probability expression, indicating the advan-tage of our proposed transmission technique over traditionaltransmissions. The effect of I-CSI is also studied throughan asymptotical PEP analysis. We mathematically proved theexistence of an error floor on the performance metrics underthe I-CSI assumption for such scenarios. Our results show thatI-CSI severely degrades the performance for low diversity gaintransmission when assuming a single relaying vehicular deploy-ment and results in error floors. However, due to extra spatialdiversity extracted when using our transmission technique withmultiple relaying vehicles, our scheme shows superior per-formance in suppressing the error floor for various SNR ranges.

APPENDIX

PAIRWISE ERROR PROBABILITY WITH

IMPERFECT CHANNEL ESTIMATION

Here, we analyze the effect of imperfect channel state in-formation of the PEP expression. We consider the PEP of thecooperative S → D and S → R → D(25). Let esd and esrd =esrerd denote the imperfect channel information/estimationerror vectors for the associated links. Therefore, we can write

[hsd hsrd]H︸︷︷︸

h

= [hsd hsrd]H︸︷︷︸

h

− [esd esrd]H︸︷︷︸

e

(A.1)

where hsd and hsrd = hsrhrd are the “estimated” channelvectors for the direct S → D channel and the indirect S →


R → D channel, respectively. In the case of imperfect channelestimation, the output of the ML detector at the destinationterminal can be expressed as

YC = hHQY =

√Esh

HQhQS+ hH

Qn (A.2)

where hQ =∑Q

q=0 diag[D(wq),D(wq)]diag[H(0)sd,q,H

(0)srd,q],

and hQ is its estimate related to each other through hQ =

hQ + eQ. The estimate can be further rewritten as

hHQ = diag

⎡⎣( Q∑

q=0

H(0)sd,q

)H

,

(Q∑

q3=0

H(0)srd,q3

)H⎤⎦

︸︷︷︸Channel matrix

× diag[(D(wq))

H , (D (wq3))H]

︸︷︷︸Doppler spread matrix

(A.3)

where [H(0)sd,q(n)]k,m=hrd,q(n; k−m) and [H

(0)srd,q(n)]k,m=

hsr(n)hrd,q(n, k −m), k,m ∈ [0, Nt − 1]. Since the Dopplerspread matrix is calculated with respect to a known veloc-ity, estimation is needed only over the channel matrix. Not-ing eQ =

∑Qq=0 diag[D(wq),D(wq)]× diag[ehsd,q, ehsrd,q],

where ehsd,q and ehsrd,q are the estimation errors for channel

matrices H(0)sd,q and H

(0)srd,q , respectively, we have

YC =√

EshHQhQS+ hH

Q(√EseQS+ n). (A.4)

The second term includes the effect of both noise and im-perfect estimation and is zero-mean complex Gaussian with thevariance of Esσ

2eQ

+N0. The average of the effective SNR (γ),including the effect of both noise and imperfect estimation, isthen given by

E(γ) =γ∣∣∣σ2

hQ+ σ2

eQ− E(hQeQ)

∣∣∣γσ2

eQ+ 1

(A.5)

where the estimation error variance vector σ2eQ

is [37]

σ2eQ

= γσ2hQ

−wHc R

−1c wc (A.6)

we have wc and Rc as the MMSE optimum filter coefficientsand the channel autocorrelation, respectively [37]. Using theresults from [40], we can rewrite (A.6) as

σ2eQ

=

((γc

|E|21)−1

+ |Es|2RHI−1R

)−1

(A.7)

where R is the normalized channel autocorrelation.Under the assumption of the I-CSI, the conditional PEP can

be therefore revised as

PCoopi(S → S|h)

∣∣∣I−CSI

≈3∑

m=1

εm

×exp

⎛⎝−(

hHsdχhsd+Gridh

Hrid

χhrid

) ρmγ

4(ρmγσ2

eQ+1

)⎞⎠. (A.8)

For sufficiently high SNR, i.e., by taking the limit of γ → ∞,we have

limγ→∞

PCoopi(S → S|h)

∣∣∣∣I-CSI

≈3∑

m=1

εm exp(−hH

sdχhsd +GridhHrid

χhrid

)(A.9)

which becomes independent of γ and indicates the presenceof an error floor. As observed in Fig. 6 and the analyticaljustification presented in the Appendix, the imperfect channelestimate degrades the performance for the error rate perfor-mance, resulting in error floors in the performance curveswhen no relay selection is performed. The error floor resultsin wasting the transmitted power, whereas the performance willbe saturated at a constant level; in other words, it means thateven by increasing the transmitted power, the performance willnot be improved. The main reason is that the asymptoticalperformance metrics become independent of the SNR γ andresult in the presence of the error floor. On the other hand,due to extra spatial diversity achieved by our proposed relayselection scheme, we are able to suppress the error floor in thesimulated SNR ranges by increasing the number of the involvedrelays and, hence, extracting additional virtual MIMO spatialdiversity.

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Mohamed F. Feteiha (S’10–M’12) received theB.Eng. and M.Eng. degrees (both with Excellencegrade with Honor) in telecommunications engineer-ing from the Arab Academy for Science and Tech-nology and Maritime Transport, Alexandria, Egypt,in 1999 and 2006, respectively, and the Ph.D. degreein electrical and computer engineering from the Uni-versity of Waterloo, Waterloo, ON, Canada, in 2012.

He is an Assistant Professor and a Researcherwith the Informatics Research Institute (IRI), City ofScientific Research and Technological Applications

(CSRTA), Alexandria. He is a Visiting Researcher and was previously aPostdoctoral Research Fellow and Team Leader with the TelecommunicationsResearch Lab, Queen’s University, Kingston, ON. He is working in the area ofwireless networks architecture, deployments, and performance evaluation. In2012, he was a Research and Teaching Associate with the Wireless Communi-cation Systems (WiComS) Research Lab, University of Waterloo. From 2000to 2008, he was an Assistant Researcher with IRI-CSRTA. During this period,he intensively worked on distributed and wireless systems and networks. Hisrecord spans more than 20 publications as a first author in top IEEE journalsand conferences.

Hossam S. Hassanein (S’86–M’90–SM’06) re-ceived the Ph.D. degree in computing science fromthe University of Alberta, Edmonton, AB, Canada,in 1990.

He is a leading authority in the areas of broad-band, wireless and mobile networks architecture,protocols, control, and performance evaluation. Hisrecord spans more than 500 publications in jour-nals, conferences, and book chapters, in additionto numerous keynotes and plenary talks at flagshipvenues. He is also the Founder and Director of the

Telecommunications Research Lab, School of Computing, Queen’s University,Kingston, ON, Canada, with extensive international academic and industrialcollaborations.

Dr. Hassanein is an IEEE Communications Society Distinguished Speaker(Distinguished Lecturer 2008–2010). He is currently the Chair of the IEEECommunication Society Technical Committee on Ad hoc and Sensor Networks.He has received several recognitions and best papers awards at top internationalconferences.

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