Page 1

International Journal of Electronics, Communication & Instrumentation Engineering Research and Development (IJECIERD) ISSN 2249-684X Vol. 2 Issue 4 Dec - 2012 1-10 Š TJPRC Pvt. Ltd.,



Dept. of Electronics & Communication Engineering,

A.S.E.T, Amity School of Engineering & Technology, Noida, UP, India 2

Dept. of Electronics & Communication Engineering,

GNIT, Greater Noida Institute of Engineering& Technology, Noida, UP, India

ABSTRACT This paper gives an overview of cooperative communication which is a technique that acts as virtual MIMO. In this paper we analyze cooperative communication in detail. We study the steps to cooperation. We analyze different combining strategies at the destination. We have calculated and plotted outage probability for a particular type of cooperation and plotted it as a function of mean SNR.

KEYWORDS: Mcr, Relays, Coded Cooperation INTRODUCTION In wireless communication networks, direct transmission between the source and destination requires high transmitted power which is very costly and it makes the network life shorter i.e. battery drain becomes fast and interference increases. Fading also occurs which reduces the signal strength going through the channel and the signal received at the destination differs from original signal transmission. MIMO uses diversity technique to offer significant increase in data throughput and link range without additional bandwidth or transmit power. Diversity technique reduces the fading by transmitting the data or information over multiple paths or multiple independent fading channels. These multiple copies of the same transmitted signal are combined at the receiver using any of the diversity technique i.e. maximum ratio combining or equal gain combining diversity etc. Cooperative communication provides transmit diversity to single antenna users. Cooperative communication is a technique to minimize the effects of fading. In this technique, multiple faded versions of a signal are transmitted from source to destination. The communication provided by cooperation is reliable. As compared to wired communication medium, there are so many factors in wireless communication medium which negatively affect the quality of the original transmitted signal, and the received signal at the destination is not the same as transmitted signal from the source. Fading is one of those factors which are responsible for changes in the quality of received signal as compared to original transmitted signal. MIMO has become an important part of wireless communication due to its innumerable advantages. Although transmit diversity, which is inherent feature of MIMO, is advantageous but it may not be practical for all the scenarios. Especially due to cost, size or hardware limitations, a wireless agent, for example: a mobile phone may not be able to support multiple transmit antennas [17], [18]. In cooperative communication, more than one, cooperative users get the transmitted signal copies and retransmit those copies of the signal toward the destination. These users are called relays. Relays may be virtual antenna elements which have wide separation and wireless communication link between them. In relayed transmission, the signal or data from source follows individual transmission paths over shorter distances in wireless communication with low power requirement and more reliability.


Sindhu Hake Gupta & S. N. Sharan

The signals from all transmitting relays are combined at the destination as multiple transmissions of same signal to reduce the performance degradation due to signal fading. Spatial diversity combining technique can be used as antenna sharing between relaying terminals, creating a virtual array to combat fading. Diversity technique can be used to combat fading phenomenon [1]. The users basically share their information and transmit with cooperation [2], [3]. Relayed transmission extends the coverage using spatial diversity to avoid fading in cooperative wireless communication system [4], [5], [6]. Relaying scheme and cooperative diversity form a virtual antenna array based on multiple antenna systems [7], [8], [9], [10], [11]. Cooperative communication allows single antenna mobiles to reap some benefits of MIMO system. So, we can summarize that cooperative communication is a technique where three or more active nodes, operating in a common wireless network, share their resources to jointly transmit messages while improving system performance through their inherent spatial diversity. The main concept behind co-operative-communication is sharing. Mobiles do have single antenna. These mobiles in a common wireless network share their antennas in a manner that will create virtual MIMO system. There are various theoretical and simulation studies examining the potential benefits of co-operativecommunication. In this technique, wireless agent who is termed as user or active node increases the effective quality of service via co-operation. Hence, cooperative communication is a technique to minimize the harmful effects of fading in which some neighbouring nodes relay the multiple copies of the same transmitted signal. This technique is possible because of broadcast nature of wireless medium, to have nodes adjacent to the source of message. As a result, nodes in the network act not only as end users but also as relays for other user messages. The multiple retransmissions of messages inherent to cooperative communication creates a spatial diversity that allows for increased throughput of reliability [17], [19]. Thus, this technique overcomes the severe impact on the system performance which arises because of fading. As the number of end users increases, it becomes extremely inefficient to use all available relays. To avoid this situation and improve the performance of cooperative relaying significant research has been done into the selection of a best relay among the multiple relays to participate in any given communication.

STEPS TO COOPERATION This section elaborates the cooperation procedure which is actually implemented in real time. It is assumed that each node in the network has a distinct identification number. The steps involved in realizing this cooperation are as follows: STEP: 1 Neighbour Maintenance Step: Each node(S) in the cluster will broadcast at a regular interval COR(Cooperative Request).This will be broadcasted on a control channel ,and will be received by all the neighbouring nodes (NN) which are within the transmitting range. Once COR is transmitted there are two probable conditions. One is that the node which has received COR will cooperate and the other condition is that the node is loaded with traffic and energy constrains also exist, thus this node will not cooperate. If it is ready to cooperate with the requesting node it will send AOC(agree on cooperation),along with it will send its own its own user ID. In this way each requesting nodes will get cooperating nodes. The requesting nodes will store the ID of cooperating nodes, and in this way will maintain neighbour set.

Analysis of Cooperation and Simulation of Performance in Cooperative Communication


Fig. 1: Neighbor Maintenance Step STEP2: Information Exchange Step: When AOC is received by the requesting node, it plans to transmit the information. Now the cooperating node, which is destination may be free or may be heavily occupied with its own assignments. In order to check whether it is ready to receive information the requesting node will send TR (transmission request).If it is ready to receive information it will send necessary information like CSI etc.

Fig. 2: Information Exchange Step STEP 3 :Local Distribution setup: After all these steps node selection and data/power allocation is done with one of the proposed algorithms. Finally data is broadcasted to each of the selected scheduled nodes, and thus cooperation is achieved & established.

Fig. 3: Local Distribution Step


Sindhu Hake Gupta & S. N. Sharan

COMBINING STRATEGIES As we have seen in fig.3 and concluded from the previous sections that there is more than one incoming transmission with the same burst of data, the incoming signals have to be combined and the original signal which was transmitted have to be retrieved. The possible strategies by which we combine these signals are as follows: 1 ERC (Equal Ratio Combining) .This is self explanatory, all the incoming signals are combined or rather they are just added up .This is done when computational time is crucial. This is the easiest way as no special computation is involved, but the performance is not satisfactory.

2 FRC (Fixed Ratio Combining) Instead of just adding up incoming signals, they are weighted with a constant rate. They will not change during communication.

3 SNRC (Signal to Noise Ratio Combining) Signals are weighted in an in an intelligent way .A very important parameter SNR is used to weight the incoming signal.

4 MRC (Maximal Ratio Combining)This technique is also termed as ratio squared combining .It is most commonly implemented and is complex to realize.

COOPERATIVE RELAYING STRATEGIES On the fundamentals of signal processing and the manner in which forwarding is achieved at the relays, cooperative relaying strategies can be broadly classified in two types:

1 .Transparent relaying technique 2. Regenerative relaying technique

Transparent Cooperative Relaying technique scaling/amplification or phase rotation. We can conclude that transparent relaying technique performs linear transformation of a signal at relay node example Amplify & Forward where as regenerative relaying include modification of a single waveform at the relay node example decode and forward, coded cooperation etc. Cooperative relaying strategies are used to relay the multiple copies of the transmitted signal from the source node toward the destination node for minimizing the effects of fading to get the reliable communication. Vander Maulen [12], [13] proposed the classical relay channel as a class of three terminal communication channels. Cover and EI Gamal [14] determine channel capacity for discrete memoryless and additive white Guassian noise channels. Lower bounds on capacity were determined by them using cooperation in which the source message is fully decoded by relay and relayed message is transmitted to the destination jointly with source. When source to relay channel quality is very high, then cooperation gives highest achievable rate. Schein and Gallager [15], [16] showed different extensions to the case of multiple relays in their work. A multiple access channel is considered by Kramer and Wijngaarden. In this channel, different sources communicate to a single destination and only a single relay is shared. Multiple access channel are examined by King Carleial, and Willems et al with generalised feedback. In generalised feedback, sources are allowed to act as relays for one another. Sendanaris et al. called the approaches of model of [20], [21] the user cooperation diversity

Analysis of Cooperation and Simulation of Performance in Cooperative Communication


[4], [2], [3] in the presence of multipath fading. Full diversity is achieved by cooperative protocols in which probability of outage reduces in proportion to 1/SNR2 while without cooperation it reduces in proportion to 1/SNR. Cooperative diversity becomes effective due to some level of synchronization. The way in which the information is transmitted from the source terminal to the relay terminal, and the way it is processed at the relay terminal the existing cooperative protocols can be divided into three types such as amplify and forward, detect and forward and coded cooperation. Amplify and Forward: In this cooperative relaying strategy, when a signal from source to destination is transmitted directly, then the copies of the same signal are transmitted to the relaying nodes (i.e. neighbouring nodes) and these relaying nodes amplify the received signal and forward it toward the destination. The relay terminals simply re-transmit a scaled version of the signal that they receive from the source terminal to the destination terminal. Depending on the scaling factor, the AF relaying scheme can be further divided into two types which are called fixed gain AF system and variable gain amplify and forward system.

Fig.1: Amplify and Forward-Communication Detect and Forward: In this method, a user attempts to detect the partner’s bits and then retransmits the detected bits. The partners are decided by a certain protocol. Each user should have a partner that is the important thing. This partner will provide diversity path. In the first and second intervals, each user transmits its own bits. Each user then detects the other user’s second bit. In the third interval, both users transmit a linear combination of their own second bit and the partner’s second bit [22]. The transmit power for the first, second and third intervals are variable and by optimizing the relative transmit power according to the conditions of the uplink and enter user channels, this method provides adaptability to channel conditions. This signalling has the advantage of simplicity and adaptability to channel conditions.

Fig. 2: Detect and Forward Communication

Coded Cooperation: In coded cooperation [23], each user attempts to transmit an incremental redundancy for its partner. Todd E. Hunter and Aria Nostratinia [24] proposed coded cooperation technique. In this method cooperative signalling is


Sindhu Hake Gupta & S. N. Sharan

integrated with channel coding. It works by sending different portions of each user’s code via two independent fading paths. Each user, instead of repeating the received bits, tries to transmit incremental redundancy for its partner. Whenever this is not possible, the user automatically reverts to no cooperative mode. All this is managed through code design, without any feedback between the users. The users segment their source data into blocks which are encoded with an error detection code e.g. CRC code. Each block is then encoded with FEC ( forward error correcting code) so that for an overall rate R, we have N total coded symbols allocated for each source block, then two users cooperate by dividing the transmission of their coded source blocks into two successive time segments which we call frame. For the first frame each user transmits at rate R1>R code word with N1=K/R1 bits. This higher rate code can be obtained, for example by puncturing the original code word. Each user receives and decodes his partner’s first frame. If the user successfully decodes the partner’s rate R1 code word, then the user computes and transmits N2 additional parity bits for the partner’s data in the second frame (N1+N2=N) for example, if the first frame was obtained via puncturing, these N2 bits could be the puncture bits left out of the first frame. Whenever a user is unable to successfully decode his partner’s message, the user will revert to a non- cooperative mode by calculating its own N2 bits and transmitting-them.

Fig. 3: Coded Cooperative Communication

In coded cooperation, each user always transmits a total of N bits per source block over the two frames, and the users only transmit in their own multiple access channels. We define the level of cooperation as N2/N which is the percentage of total bits per each source block that the user transmits for his partners. Cooperative communication systems are much flexible than conventional communication systems [25].

SYSTEM PERFORMANCE ANALYSIS To analyze the performance of cooperative communication wireless network, the bit error rate (BER) and probability of outage of the system are considered for a given transmitted signal to noise ratio (SNR). Average Signal-to-Noise Ratio (γ): It is measured at the output of the receiver and is thus related directly to the data detection process itself. It is easiest to evaluate among all the existing system performance measures. It serves as an excellent indicator of the overall fidelity of the system. The term noise in signal to noise ratio refers to the thermal noise which is present at the input of the receiver in the context of a communication subject to fading impairment. The more appropriate performance measure is average SNR (γ) where the word average refers to statistical averaging over the probability distribution of the fading.

Average SNR,


(γ) dγ


Analysis of Cooperation and Simulation of Performance in Cooperative Communication

P γ (γ) denotes the probability density function (PDF) of γ. Outage Probability (Pout): It is defined as the Probability that the instantaneous error probability exceeds a specified value or equivalently, the Probability that the output SNR, Υ, falls a certain specified threshold

th. Threshold value of total

SNR is the value at which system is not in outage. Outage is a condition of a system at which user becomes unable to get the proper service from the system. Pout =


Pout is equal to the CDF (cumulative distribution function of evaluated at

, namely, P


th .

Average Bit Error Probability (BEP): It is the most difficult of the three to compute. On the other hand, it is the one that is most revealing about the system behaviour. The difficulty arises in its evaluation because of the fact that the conditional BEP is in general a non-linear function of the instantaneous SNR, the nature of the nonlinearity being a function of the Modulation/detection scheme employed by the system. Thus we sum up that cooperative communication is an emerging paradigm where multiple mobiles share their bandwidth and power (resources) to achieve better overall performance. Performances can be investigated by different measures as discussed above. We are going to investigate and analyse the performance of different cooperative communication schemes under different fading environments. Continuous research is done to analyse and improve the performance of decode and forward [25], [26], [29], [30], [35], amplify and forward [26], [27], [28], [37] and coded cooperation [31], [32], [33], [34], [36].

CONCLUSIONS This paper presents an extensive insight into cooperative wireless communication, which creates a virtual MIMO and helps single antenna users to achieve spatial diversity. Three types of cooperative signaling techniques are explained with their advantages in this paper. System performance evaluation is also done. Research till now indicates that the cooperative communication is going to be the characteristic feature of wireless communication


Azizollah Jamshidi, Masoumeh Nasiri-Kenari, Abbas Taherpour, “Outage analysis of a coded c ooperative OFDM system in multipath Rayleigh Fading channels”, in 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, 2007.


Sindhu Hake Gupta & S. N. Sharan


A. Sendonaris, E. Erkip, and B. Aazhang, “User cooperation diversity-Part I: System description”, IEEE Trans. Commun., vol. 51, No. 11, pp. 1927-1938, Nov. 2003.


A. Sendonaris, E. Erkip, and B.Aazhang, “User Cooperation Diversity-Part II: Implementation aspects and performance analysis”, IEEE Trans. Comm., vol. 51, no. 11, Nov. 2003.


A. Sendonaris, E. Erkip, and B.Aazhang, “Increasing uplink capacity via user cooperation diversity”, in proc. IEEE Int. Symp. Information Theory (ISIT’98), Cambridge, MA, Aug. 1998, p. 156.


J. N. Laneman and G. W. Wornell, “Energy efficient antenna sharing and relaying for wireless networks”, in proc. IEEE Wireless communications and Networking Conf. (WCNC’00). Chicago, II, Oct. 2000, pp.7-12.


V. Emamian and M. Kaveh, “Combating shadowing effects for systems with transmitter diversity by using collaboration among mobile users”, in proc. Int. Symp. Communications (ISC’01), Taiwan, Nov. 2001, pp. 105.1105.4.


I. E. Telatar. (1995), “Capacity of multi antenna Guassian channels”, Tech. Rep. Bell Labs, Lucent Technologies. [Online]. Available:


I. E. Telatar. (1995), “Capacity of multi antenna Guassian channels”, Europ. Trans. Telecommun., vol. 10, pp. 585-596, Nov.-Dec. 1999.


A. Narula, M. D. Trott, and G. W. Wornell, “Performance limits of coded diversity methods for transmitter antenna arrays”, IEEE Trans. Information theory, vol.45, pp. 2418-2433, Nov. 1999.

10. L. Zheng and D. N. C. Tse, “Diversity and freedom: A fundamental tradeoff in multiple antenna channels”, in Proc. IEEE Int. Symp. Information Theory (ISIT), Lausanne, Switzerland, June/July 2002, p. 476. 11. L. Zheng and D. N. C. Tse, “Diversity and multiplexing: A fundamental tradeoff in multiple-antenna channels”, IEEE Trans. Information theory, vol. 49, pp. 1073-1096, May 2003. 12. E. C. Vander Meulen, “Transmission of Information in a T-Terminal Discrete memoryless channel”, Berkeley, CA: Dept. Statistics, University California, 1968. 13. E. C. Vander Meulen, “Three terminal communication channels”, Adv. Appl. Probab., vol. 3, pp. 120-154,1971. 14. T. M. Cover and A. A. El Gamal, ”Capacity theorems for the relay channel”, IEEE Trans. Information Theory, vol. IT-25, pp. 572-584, September 1979. 15. B. Schein and R. G. Gallager, “The Guassian parallel relay network”, in proc. IEEE Int. Symp. Information Theory (ISIT), Sorrento, Italy, June 2000, p.22. 16. B. Schein, “Distributed coordination in network information theory”, Ph.D. dissertation, MIT, Cambridge, MA, 2001. 17. Aria Nostratinia, Ahmadreza Hedayat, “Cooperative Communication in Wireless Networks”In IEEE Communication Magazine, October 2004, pp.74-78. 18. M.Uysal,“Cooperatative communication for Improved Wireless Network Transmission, “Information science reference, july 2009. 19. A.Meier, “Cooperative Diversity in Wireless Networks,” MSC thesis University of Edinburg, 2004.

Analysis of Cooperation and Simulation of Performance in Cooperative Communication


20. A.B. Carleial, “Multiple access channels with different generalized feedback signals”, IEEE Trans. Inform. Theory, vol.IT-28, pp. 841-850. Nov. 1982. 21. F. M. J. Willems, E. C. vander Meulen, and J. P. M. Schalkwijk,”An achievable rate region for the multiple access channel with generalized feedback”, in Proc. Allerton Conf. Communications, Control, and Computing, Monticello, IL, Oct. 1983. Pp. 284-292. 22. G.K. Karagiannidis, “Editorial Multiuser Cooperative Diversity for Wireless Networks,”EURASIP journal on wireless communication and networking. 23. Todd E. Hunter and Aria Nosratinia, “Diversity through Coded Cooperation”, IEEE Trans.on Wireless Communications, Volume 5, Issue 2, Feb. 2006 pp:283-289. 24. Todd E. Hunter, Aria Nosratinia, “Distributed Protocols for User Cooperation in Multi-User Wireless Networks” in Globecom, IEEE Communication Society, pp. 3788-3792. 25. Min-Kuan Chang, Shi-Yong Li, “Performance Analysis of Cooperative Communication System with Hierarchical Modulation over Rayleigh Fading Channel” in IEEE Tran. On Wireless Communication Vol.8, No.6, June2009,pp. 2848-2852. 26. Michael R. Souryal, Branimir R. Vojcic, “ Performance of Amplify and Forward and Decode and Forward Relaying in Rayleigh fading with Turbo Codes” in ICASSP, IEEE 2006, pp.681-684. 27. Y Wu, Matthias Patzold, “Performance Analysis of Cooperative Communication System with Imperfect Channel Estimation” in IEEE, ICC 2009 proceedings. 28. YiWu, Matthias Patzold, “Performance Analysis of Amplify and forward Cooperative Communication System with Channel Estimation Errors” in ICCS, IEEE 2008, pp.1620-1624. 29. Shaolei Ren, K.B.Letaief, “Outage Reduction in Cooperative Networks with Limited Feedback” in IEEE Tran. On Communication, Vol.58, No.3, March 2010, pp.748-752. 30. Josephine P.K. Chu, Raviraj S. Adve, “Using the Bhattacharyya Parameter for Design and Analysis of Cooperative Wireless System” in IEEE Tran. On Wireless Communication, Vol.8, No.3, March 2009, pp. 13841395. 31. Mohammad Janani, Ahmdreza Hedayat, Todd E. Hunter, Aria Nosratinia, “Coded Cooperation in Wireless Communication: Space-Time Transmission and Iterative Decoding” in IEEE Tran. On Signal Processing, Vol.52, No. 2, Feb 2004, pp.362-371. 32. Todd E. Hunter, Shahab Sanayei and Aria Nostratinia, “Outage Analysis of Coded Cooperation”, in IEEE Tran. On Information Theory, Vol.52, No.2, Feb 2006, pp. 375-391. 33. Ankur Bansal, Parul Garg, “Performance Analysis of Coded Cooperation Under Nakagami-m Fading Channels”, in Proceedings of ICC, IEEE. 34. Zhang Hong, WU Dao-long, “Two-User Cooperative Diversity Strategies and their Performance Analysis” in ICSP, IEEE 2010 Proceedings, pp.1581-1584. 35. Kamel Tourki, Hong-Chuan Yang and Mohammed-Slim Alouini, “Accurate Outage Analysis of Incremental Decode-and-Forward Opportunistic Relaying” in IEEE Tran. On Wireless Communication, Vol. 10, No.4, April 2011, pp.1021-1025. 36. AndrejStefanov,ElzaErkip,“Cooperativ--e Coding for Wireless Networks”, in IEEE Tran. On Communications, Vol. 52, No.9, September 2004, pp.1470-1476.


Sindhu Hake Gupta & S. N. Sharan

37. Foroogh S. Tabataba, Parastoo Sadeghi, Mohammed Reza Pakravan, “Outage Probability and Power Allocation of Amplify and Forward Relaying with Channel Estimation Errors” in IEEE Tran. On Wireless Communication, Vol. 10, No.1, January 2001, pp.124-133. 38. N. L. Johnson, S. Kotz, and N. Balakrishan, Continuous Univariate Distribution, 2nd ed. New York: Wiley, 1995, vol. 2. 39. Theodore S. Rappaport, “Wireless Communications principles and practice”, second edition, ISBN, United States: Prentice Hall publication, 2002, 1996, reprint 2010, Dorling Kindersley (India) Pvt. Ltd. pp. 212-214.

1. ECE - IJECIERD - final