MIMO-OFDM in a Cooperative Frequency Selective Fading Channel: an Analysis

Hindawi Journal of Computer Networks and Communications Volume 2019, Article ID 9107505, 7 pages https://doi.org/10.1155/2019/9107505

Research Article MIMO-OFDM in a Cooperative Frequency Selective Fading Channel: An Analysis

Saurabh Dixit ,1 Himanshu Katiyar,2 and Arun Kumar Singh 3

1Department of Electronics, CIPET, Lucknow, India 2Department of Electronics, REC, Sonebhadra, India 3Department of Electronics, REC, Kannauj, India

Correspondence should be addressed to Saurabh Dixit; [email protected]

Received 2 January 2019; Revised 8 June 2019; Accepted 17 September 2019; Published 3 November 2019

Academic Editor: Youyun Xu

Copyright © 2019 Saurabh Dixit et al. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Diversity is an integral technique in the advancement of wireless communications. Multiple-input multiple-output (MIMO) technology continues to occupy a pivotal position in the fourth-generation (4G) standards and beyond. With the advent of full- dimension (FD) MIMO and massive MIMO, new paradigms are being targeted in the disruptive technology. )ird-generation partnership project (3GPP) has also emphasized the advanced MIMO technology for achieving its vision of International Mobile Telephony (IMT) 2020. However, implementing multiple antennas can be cumbersome due to limiting factors such as size, cost, and hardware limitations. In such a scenario, where implementing many antennas is not feasible, the benefits of diversity can be reaped by employing cooperative communication. Single antenna mobiles can share their mobile stations giving rise to a virtual MIMO system. In this paper, the authors have analyzed the performance of cooperative mobile stations in a frequency selective channel. Orthogonal frequency division multiplexing (OFDM), known for its robustness against multipath fading, is the modulation technique employed to circumvent the hazardous effects of intersymbol interference (ISI). MIMO is the key technology for achieving capacity advantage and improving link reliability. )e blend of these two cutting edge technologies in a cooperative relay system presents encouraging results and inferences. Maximal Ratio Combining (MRC) is employed at the relay and the receiver to maximize the signal-to-noise ratio (SNR). Decode and forward (DF) protocol is used at the relay node owing to its regenerative nature and adaptability. )e closed-form expressions for bit error rate (BER) and outage probability (Pout) are derived and compared with the experimental results. )e effect of power decay factor is analyzed and simulation is done for 2 × 2, 2 × 3, and 2 × 4 transmit and receive antennas. )e number of antennas at the relay is kept as 2. )e analytical results agree with the simulation results.

1. Introduction where implementing multiple antennas is not feasible, the beneﬁts of diversity can be reaped by employing cooperative Wireless communications have exploited the diversity communication. Single antenna mobiles can share their technique since its inception and continues to capitalize on mobile stations giving rise to a virtual MIMO system. In this its multiple advantages in beyond fourth-generation (4G) paper, we have analyzed the performance of cooperative standards. With the advent of full-dimension (FD) MIMO mobile stations in a frequency selective channel. Orthogonal and massive MIMO, new paradigms are being targeted in the frequency division multiplexing (OFDM), known for its disruptive technology. )ird-generation partnership project robustness against multipath fading, is the modulation (3GPP) has also emphasized the advanced MIMO tech- technique employed to circumvent the hazardous eﬀects of nology for achieving its vision of International Mobile Te- intersymbol interference (ISI). lephony (IMT) 2020. However, implementing multiple )e concept of relaying was introduced by Van Der antennas can be cumbersome due to limiting factors such as Meulen in 1971 [1]. Cover and Gamal [2] provided an insight size, cost, and hardware limitations. In such a scenario, into relay-based communication by analyzing the capacity of 2 Journal of Computer Networks and Communications three node network, namely, the source, destination, and relay. e channel considered was additive white Gaussian noise (AWGN). Tarokh et al. in 1999 [3] introduced the concept of space-time block codes by the use of orthogo- nality. In their landmark paper, Gupta and Kumar in 2000 [4] have statistically analyzed the theoretically oered Relay throughput for large-scale relay networks. Sendonaris et al. in 2003 [5] proposed the concept of implementing spatial diversity without using multiple antennas. e fundamental principle is to eciently establish multiple antennas in a Destination distributed manner by allowing neighboring nodes to share Source their radio resources. Space-time cooperation can be applied Figure to a broad range of applications, such as broadcast networks, 1: Cooperative relay model. sensor networks, and relay networks. Nosratinia et al. in 2004 [6] have provided a vivid overview of cooperative bits are modied and, based on a decision, relayed towards communication technique. e authors opine that as long as the destination. Decode and forward (DF) protocol is the the fading path is uncorrelated, the benets of spatial di- commonly employed regenerative protocol, where the in- versity are reaped. e trade-o between total power con- formation bits are detected, decoded, and sent forward. is sumed and sum rate is analyzed. A comparison of scheme has been shown to reduce channel interference and performance in cooperation signaling methods is provided. additive noise at the relay as demonstrated by Lu et al. in e results demonstrate that coded cooperation and decode 2010 [14]. Furthermore, Hasna and Alouini in 2003 [15] and forward scheme perform better than without co- demonstrated in their pioneering work that regenerative operation. Dohler et al. in 2003 [7] have introduced the protocols perform better at low SNR. However, regenerative concept of multihop distributed MIMO and deduced ca- protocols require more complex baseband processing. pacity advantages over SISO multihop communication. In the transparent relaying protocols, also known as Laneman et al. in 2004 [8] have comprehensively evaluated nonregenerative protocols, there is no modication in the the diversity order of multiple access protocols such as information bits. Power scaling and phase rotation are done amplify and forward protocol and decode and forward at the relay before forwarding the signal. Amplify and protocol. e authors have demonstrated that cooperation forward (AF) protocol is the commonly employed non- yields full spatial diversity. Cui et al. in 2004 [9] have regenerative protocol, where the information bits are am- demonstrated that over certain distance ranges, the total plied and sent forward. However, noise is also amplied energy consumption can be reduced. However, in short and relayed in this protocol. erefore, this protocol is not ranges, the performance of single-input single-output suitable for low SNR. (SISO) may outperform multiple-input multiple-output e key contributions of this paper are as follows: (MIMO) or cooperative MIMO as far as energy eciency is concerned. Shin et al. in 2007 [10] designed an OFDM space- (i) An OFDM-modulated MIMO relay channel with time cooperative system and achieved results demonstrating MRC at relay and destination is developed a signicant performance advantage with cooperative (ii) Closed-form expressions for outage probability OFDM. Tolli et al. in 2008 [11] have investigated the per- (Pout) and bit error rate (BER) are evaluated formance of nodes in a soft handover region, receiving a (iii) e simulation results and numerical results are signal from distributed base stations cooperating amongst evaluated for power decay factor of 0, 1, and 2, themselves. e authors opine that base station cooperation respectively enhances the system complexity. Baek and Song in 2008 [12] (iv) e simulation and numerical results agree with have designed and analyzed the performance of cooperative each other, hence validating our model diversity in MIMO-OFDMA system. e authors demonstrate that high-performance gains are achieved while using (v) e signal-to-noise-ratio (SNR) is kept low to cooperative diversity. Jing and Jafarkhani in 2009 [13] have validate the ecacy of decode and forward protocol analyzed the performance of single and multiple relays and (DF) protocol evaluated their diversity order. e authors have focused on network relay selection 2. System Model schemes and propose suboptimal schemes with linear complexity. A generic cooperative relay model is depicted in Figure 2 depicts our system model where OFDM-modulated Figure 1. e mobile nodes cooperate mutually to enhance signal are transmitted using multiple antennas. Chalise and the signal-to-noise-ratio (SNR). Each mobile station or user Vandendorpe in 2008 [16] have developed a similar model collaborates with one another user or with a few users jointly for a Rayleigh ¤at-fading channel. ere is a direct link and emulates a virtual MIMO methodology concept. between source and destination characterized by channel e protocols employed in cooperative communications hsd. e channel between source and relay is dened as hsr. can be classied into generative and transparent relaying e channel between source and destination is hsd while that protocols. In generative relaying protocols, the information between relay and destination is hrd. e communication Journal of Computer Networks and Communications 3

Relay

Mr Slot 1 Slot 2 MRC Encode

hsr hrd Destination

M OFDM t Md OFDM MRC mod Source demod hsd Figure 2: System model (MIMO-OFDM with relay nodes). between source and destination occurs in orthogonal space- Table 1: Simulation parameters of OFDM-MIMO relay channel. time block coding (OSTBC). e following assumptions Simulation parameter Value hold true in our model: Number of transmit 2 (i) e number of antennas at the source, relay, and antennas (Mt) Number of relay destination are Mt,Mr, and Md, respectively. 2 h ,h , h antennas (Mr) (ii) e channels sd sr and rd are independent and Number of destination identically distributed with zero mean and circularly 2/3/4 antennas (Md) symmetric complex Gaussian (ZMCSG). Combining scheme MRC (iii) e distance between source and destination is Modulation BPSK signicant as compared to that between relay and Number of channel taps 2 Frequency selective Rayleigh destination. Channel fading channel (iv) e instantaneous SNR at the relay is greater than Number of Monte Carlo 2e6 the threshold SNR required for relay mode to be simulation active. SNR (dB) 18 to 0 dB − (v) In the rst time slot, the source sends its orthogonal signals to relay and destination. e relay uses MRC is the ratio of Bit energy E to noise spectral density N , and decodes the signal. ( b) ( o) and c is the instantaneous SNR. e PDF of SNR from the (vi) In the second time slot, the relay transmits the relay to the destination can be dened as follows: decoded signal to the destination which combines 1 c /4 S the signal of two time slots. p c e(− ) rd , crd ( )4 rdS ( ) Ω 3. Numerical Expression and Modeling where pc c is the PDF of SNR from the relay to the2 rd( ) Ω destination, is the sum of average channel power from Table 1 outlines the simulation parameters of the model. rd relay to the destination, S is the ratio of Bit energy E to e number of antennas at the source and the relay is kept ( b) noise spectral density N , and c is the instantaneous SNR. the same for uniform space-time coding. e antennas at Ω ( o) e cumulative distribution function (CDF) of the PDF is the relay and destination perform MRC to enhance the c /4 S evaluated by its integral such that P c 1 e(− ) sr ; SNR. Binary phase shift keying (BPSK) modulation is sr( ) − therefore, the CDF of the source to relay to the destination employed for simplicity. e channel is frequency selective Ω will be dened by (3) as done in Papoulis and Pillai in 2002 in nature with two-channel taps with Rayleigh [18]: distribution. P c 1 1 Pc c 1 Pc c , srd( ) − − sr( ) − rd( ) ( ) 4. Mathematical Modeling where P c is the CDF of source-relay-destination. For srd( ) 3 IID, c c c. erefore, the CDF in (3) can be sim- We start our modeling by taking the probability density sr rd function (PDF) of a Rayleigh frequency selective channel plied as n dened in (1) as described by Dixit and Katiyar in 2018 [17]: P c 1 1 P c , srd( ) − − c( ) ( ) 1 c /4 srS pc c e(− ) , sr where n Mt Mr is the number of antennas at the relay. ( )4 srS ( ) 4 Ω For M M number of independent and identical fading d where pc c is the PDF of SNR from source to relay, 1is antennas at the destination, the CDF in (4) is modied as sr( ) Ω sr the sum of average channel power from the source to relay, S follows: Ω 4 Journal of Computer Networks and Communications

n M n M )erefore, the BER can be expressed as described by P (c) � − − P (c) � − − e(− c)/4ΩS . srd �1 �1 c � � �1 � � � Goldsmith ([19], equation (6.50)): (5) ∞ BER � � Ps(c)pc (c)dc 0 srd Upon diﬀerentiating (5), we obtain the PDF of SNR of the source to relay to the destination as follows: M− 1 M − 1 mn ∞ �� Q( 2c )� − e−((− cnk)/4ΩS)�e−(cn)/4ΩSdc, n M− 1 d n 4ΩS 0 p (c) � M�1 − �1 − P (c) � � �1 −� − e(− c)/4ΩS � �. k�0 k csrd c dc (10) (6) where Ps(c) is the probability of error in additive white Upon diﬀerentiating (6), it can be solved as follows: Gaussian noise (AWGN). To simplify (10), the identities in (− ) S n n M− 1n− e Υ /4Ω � 5A.2 and 5A.3 in Simon and Alouini [20] are employed, as pc (c) � M�1 − �1 − Pc(c) � � illustrated in (11) and (12): srd 4ΩS m ∞ (7) a − at m− 1 �� Mn M− 1 Jm(a, b) ≜ � e t Q( 2c )dc, (11) � �1 − e−(cn)/4ΩS � �− e− cn/4ΩS �. Γ(m) 0 4ΩS √�� c/π To solve (7), we use the binomial relation J (a, b) J (c) � m ≜ m m+(1/2) M 2(1 + c) M M k �1 − e− t � � � �− e− t � . (8) Γ(m +(1/2)) 1 1 k · F �1, m + ; m + 1; �. k�0 Γ(m + 1) 2 1 2 1 + c )us, (7) can be written as (12)

M− 1 M − 1 k Mn Here, c � b/2a, Γ (·) is the gamma function such that (− cn)/4ΩS (− cn)/4ΩS ∞ − t a− 1 pc (c) � � �− e � e . (9) Γ(a) � � e t dt, and F (·, ·; ·; ·) is the Gaussian srd 4ΩS 0 2 1 k�0 k hypergeometric function. Substituting (11) and (12) into (10) yields the closed-form expression of BER as follows:

√�� M− 1 m − 1 c/π Γ(m +(1/2)) 1 1 BER � M � (− 1)k� �� F �1, m + ; m + 1; ��, (13) m+(1/2) (m + ) 2 1 + c k�0 k 2(1 + c) Γ 1 2 1 where c � b/2a , a � n(k + 1)/4ΩS, b � 2, m � 1, n � M � where Υ is the incomplete gamma function, deﬁned as t x M ,M � M (a, x) � � ta− 1e− t t r d, the number of antennas at the destination. Υ 0 d . By substituting (15) into (14), we Equation (13) yields a closed-form expression for BER for derive the closed-form expression for Pout as follows: the channel between the source and destination via the relay. M− M − − 1 1 1 Mn n(k + 1) n(k + 1)c Here, Ω and S represent the average fading power and bit P � � � � Υ� th, 1�. out S S S energy to noise spectral density, respectively. )e average k�0 k 4Ω 4Ω 4Ω fading power is a function of power decay factor (Δ) such − (l− 1)Δ (16) that Ωl � e Δ ≥ 0, l � 1, 2, ... ,L, l is the number of the channel tap, and Ωl is the average fading power of the lth propagation path. 4.2. Direct Link (without Relay). For the direct link between the source and destination, the closed-form expression for 4.1. Outage Probability (P ). )e outage probability rela- out c Pout has been derived by Dixit and Katiyar in 2018 [17] and tive to c is deﬁned as P ≡ � th p (c)dc. )erefore, th out 0 csrd expressed as follows: M− cth 1 M − 1 c ((− cn)/4ΩS)(k+1) Mn th P � � � �− e � dc. (14) Pout � gammainc�,MtMd �, (17) out S c 0 k�0 k 4Ω where M M is the order of diversity, c is the average fading To solve (14), the integral identity in Gradshteyn and t d power as described in (13), gammainc(·, ·) is the incomplete Ryzhik ([21], equation (3.381.1)) is used as illustrated: c u gamma function, and th is the threshold SNR. )e closed- � xυ− 1e− μxdx � μ− υΥ(υ, μu), (15) form expression for BER is derived by Dixit and Katiyar [17] 0 and expressed as Journal of Computer Networks and Communications 5

OFDM MIMO relay outage prob. ∆ = 0 OFDM MIMO relay outage prob. ∆ = 2 100 100

10–5 10–5

–10

out –10 10 out 10 P P

10–15 10–15

10–20 10–20 –18 –16 –14 –12 –6–8–10 –4 –2 0 –18 –16 –14 –12 –10 –8 –6 –4 –2 0 SNR (dB) SNR (dB)

Analytical = 2, Analytical = 2, Mt Mt Analytical Mt = 2, Analytical Mt = 2, Mr = 2 direct Mr = 3 relay Mr = 2 direct Mr = 3 relay Simulated = 2, Simulated = 2, Mt Mt Simulated Mt = 2, Simulated Mt = 2, Mr = 2 direct Mr = 3 relay Mr = 2 direct Mr = 3 relay Analytical = 2, Analytical = 2, Mt Mt Analytical Mt = 2, Analytical Mt = 2, Mr = 2 relay Mr = 4 direct Mr = 2 relay Mr = 4 direct Simulated = 2, Simulated = 2, Mt Mt Simulated Mt = 2, Simulated Mt = 2, Mr = 2 relay Mr = 4 direct Mr = 2 relay Mr = 4 direct Analytical = 2, Analytical = 2, Mt Mt Analytical Mt = 2, Analytical Mt = 2, Mr = 3 direct Mr = 4 relay Mr = 3 direct Mr = 4 relay Simulated = 2, Simulated = 2, Mt Mt Simulated Mt = 2, Simulated Mt = 2, Mr = 3 direct Mr = 4 relay Mr = 3 direct Mr = 4 relay Figure 3: Outage probability with MIMO-OFDM direct and relay Figure 5: Outage probability with MIMO-OFDM direct and relay (Δ � 0). (Δ � 2).

OFDM MIMO relay outage prob. ∆ = 1 OFDM MIMO relay error prob. ∆ = 0 100 100

–2 10–5 10

–4 out –10 10

P 10 BER

–6 10–15 10

10–20 10–8 –18 –16 –14 –12 –6–8–10 –4 –2 0 –18 –16 –14 –12 –10 –8 –6 –4 –2 0 SNR (dB) SNR (dB)

Analytical Mt = 2, Analytical Mt = 2, Analytical Mt = 2, Analytical Mt = 2, Mr = 2 direct Mr = 3 relay Mr = 2 direct Mr = 3 relay

Simulated Mt = 2, Simulated Mt = 2, Simulated Mt = 2, Simulated Mt = 2, Mr = 2 direct Mr = 3 relay Mr = 2 direct Mr = 3 relay

Analytical Mt = 2, Analytical Mt = 2, Analytical Mt = 2, Analytical Mt = 2, Mr = 2 relay Mr = 4 direct Mr = 2 relay Mr = 4 direct

Simulated Mt = 2, Simulated Mt = 2, Simulated Mt = 2, Simulated Mt = 2, Mr = 2 relay Mr = 4 direct Mr = 2 relay Mr = 4 direct

Analytical Mt = 2, Analytical Mt = 2, Analytical Mt = 2, Analytical Mt = 2, Mr = 3 direct Mr = 4 relay Mr = 3 direct Mr = 4 relay

Simulated Mt = 2, Simulated Mt = 2, Simulated Mt = 2, Simulated Mt = 2, Mr = 3 direct Mr = 4 relay Mr = 3 direct Mr = 4 relay Figure 4: Outage probability with MIMO-OFDM direct and relay Figure 6: BER performance with MIMO-OFDM direct and relay (Δ �1). (Δ � 0). 6 Journal of Computer Networks and Communications

OFDM MIMO relay error prob. ∆ = 1 OFDM MIMO relay error prob. ∆ = 2 0 100 10

–2 10–2 10

–4 10–4

10 BER BER

–6 10–6 10

–8 10–8 10 –18 –16 –14 –12 –10 –8 –6 –4 –2 0 –18 –16 –14 –12 –6–8–10 –4 –2 0 SNR (dB) SNR (dB) Analytical M = 2, Analytical M = 2, Analytical Mt = 2, Analytical Mt = 2, t t M = 2 direct M = 3 relay Mr = 2 direct Mr = 3 relay r r Simulated M = 2, Simulated M = 2, Simulated Mt = 2, Simulated Mt = 2, t t M = 2 direct M = 3 relay Mr = 2 direct Mr = 3 relay r r Analytical M = 2, Analytical M = 2, Analytical Mt = 2, Analytical Mt = 2, t t M = 2 relay M = 4 direct Mr = 2 relay Mr = 4 direct r r Simulated M = 2, Simulated M = 2, Simulated Mt = 2, Simulated Mt = 2, t t M = 2 relay M = 4 direct Mr = 2 relay Mr = 4 direct r r Analytical M = 2, Analytical M = 2, Analytical Mt = 2, Analytical Mt = 2, t t M = 3 direct M = 4 relay Mr = 3 direct Mr = 4 relay r r Simulated M = 2, Simulated M = 2, Simulated Mt = 2, Simulated Mt = 2, t t M = 3 direct M = 4 relay Mr = 3 direct Mr = 4 relay r r Figure 7: BER performance with MIMO-OFDM direct and relay Figure 8: BER performance with MIMO-OFDM direct and relay (Δ �1). (Δ � 2).

√�� 1 c/π Γ(m +(1/2)) closed-form expression values of outage probability for a BER � � power decay factor of 1 and 2, respectively. )e number of am 2(1 + c)m+(1/2) Γ(m + 1) (18) transmit antennas at the source is taken as 2, the same 1 1 number of antennas at the relay, while, at the destination, 2/ · F �1, m + ; m + 1; ��, 3/4 antennas combine. 2 1 2 1 + c Figure 6 plots the closed-form expression for BER for a decay factor of 0. )e simulated results closely match the here a � 1/4ΩS, c � b/2a, m � MtMd, and b � 2. In (17) and numerical values. )e performance of direct link is com- (18), c and Ω represent the average fading power and are a pared with that of relay link and the results demonstrate an function of power decay factor as described in (13). improvement in the relay link. )e relay performance improves with the higher number of antennas at the destination. Figures 7 and 8 plot the simulated and closed-form 5. Simulation Results expression values of BER for a power decay factor of 1 and 2, respectively. )e number of transmit antennas at the source )e closed-form expressions for BER and Pout for the relay have been derived in (13) and (16). )e closed-form ex- is taken as 2, the same number of antennas at the relay, while, at the destination, 2/3 antennas combine. pressions for BER and Pout for the direct link are specified in (17) and (18), respectively. )e results obtained from (13) and (16)–(18) are plotted and compared with the simulation 6. Conclusion values. )e simulation results agree with the numerical results, thus validating our model. )e results demonstrate that cooperative relay communi- Figure 3 plots the closed-form expression for outage cation offers performance improvement and combined with probability for a decay factor of 0. )e simulated results MIMO-OFDM provides an ISI free channel. )e effect of closely match the numerical values. )e performance of power decay factor is also analyzed, and it is shown that high direct link is compared with that of relay link and the results power decay leads to degradation in performance. )e re- demonstrate an improvement in the relay link. )e relay sults in this paper are encouraging for advocating co- performance improves with the higher number of antennas operative communications as a viable option, especially at the destination. Figures 4 and 5 plot the simulated and where the implementation of MIMO is constrained. Journal of Computer Networks and Communications 7

Data Availability [16] B. Chalise and L. Vandendorpe, “Outage probability analysis of a MIMO relay channel with orthogonal space-time block )e data used to support the findings of this study are in- codes,” IEEE Communications Letters, vol. 12, no. 4, cluded within the article. pp. 280–282, 2008. [17] S. Dixit and H. Katiyar, “OFDM for 4G wireless communications,” International Journal of Interdisciplinary Telecom- Conflicts of Interest munications and Networking, vol. 10, no. 1, pp. 63–84, 2018. [18] A. Papoulis and S. U. Pillai, Probability, Random Variables, )e authors declare that they have no conflicts of interest. and Stochastic Processes, Tata McGraw Hill, New Delhi, India, 2002. [19] A. Goldsmith, Wireless Communications, Cambridge Uni- References versity Press, New York, NY, USA, 2005. [1] E. C. Van Der Meulen, “)ree terminal communication [20] M. K. Simon and M.-S. Alouini, Digital Communication over channels,” Advances in Applied Probability, vol. 3, no. 1, Fading Channels, John Wiley and Sons, Hoboken, NJ, USA, pp. 120–154, 1971. 2005. [2] T. Cover and A. E. Gamal, “Capacity theorems for the relay [21] I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series and channel,” IEEE Transactions on Information 0eory, vol. 25, Products, Academic Press, San Diego, CA, USA, 7th edition, no. 5, pp. 572–584, 1979. 2007. [3] V. Tarokh, H. Jafarkhani, and A. R. Calderbank, “Space-time block codes from orthogonal designs,” IEEE Transactions on Information 0eory, vol. 45, no. 5, pp. 1456–1467, 1999. [4] P. Gupta and P. R. Kumar, “)e capacity of wireless networks,” IEEE Transactions on Information 0eory, vol. 46, no. 2, pp. 388–404, 2000. [5] A. Sendonaris, E. Erkip, and B. Aazhang, “User cooperation diversity-part II: implementation aspects and performance analysis,” IEEE Transactions on Communications, vol. 51, no. 11, pp. 1939–1948, 2003. [6] A. Nosratinia, T. E. Hunter, and A. Hedayat, “Cooperative communication in wireless networks,” IEEE Communications Magazine, vol. 42, no. 10, pp. 74–80, 2004. [7] M. Dohler, A. Gkelias, and H. Aghvami, “2-hop distributed MIMO communication system,” Electronics Letters, vol. 39, no. 18, pp. 1350-1351, 2003. [8] J. N. Laneman, D. N. C. Tse, and G. W. Wornell, “Cooperative diversity in wireless networks: efficient protocols and outage behavior,” IEEE Transactions on Information 0eory, vol. 50, no. 12, pp. 3062–3080, 2004. [9] S. Cui, A. J. Goldsmith, and A. Bahai, “Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks,” IEEE Journal on Selected Areas in Communications, vol. 22, no. 6, pp. 1089–1098, 2004. [10] O. S. Shin, A. Chan, H. T. Kung, and V. Tarokh, “Design of an OFDM co-operative space- time diversity system,” IEEE Transactions on Vehicular Technology, vol. 56, no. 4, pp. 2203–2215, 2007. [11] A. Tolli, M. Codreanu, and M. Juntti, “Cooperative MIMO- OFDM cellular system with soft handover between distributed base station antennas,” IEEE Transactions on Wireless Communications, vol. 7, no. 4, pp. 1428–1440, 2008. [12] M.-s. Baek and H.-k. Song, “Cooperative diversity technique for MIMO-OFDM uplink in wireless interactive broadcast- ing,” IEEE Transactions on Consumer Electronics, vol. 54, no. 4, pp. 1627–1634, 2008. [13] Y. Jing and H. Jafarkhani, “Single and multiple relay selection schemes and their achievable diversity orders,” IEEE Trans- actions on Wireless Communications, vol. 8, no. 3, pp. 1414– 1423, 2009. [14] H. Lu, H. Nikookar, and T. Xu, OFDM Communication with Cooperative Relays, Communications and Networking, Intech Publisher, Rijeka, Croatia, 2010. [15] M. O. Hasna and M.-S. Alouini, “Outage probability of multihop transmission over nakagami fading channels,” IEEE Communications Letters, vol. 7, no. 5, pp. 216–218, 2003. International Journal of Rotating Advances in Machinery Multimedia

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