Coalitional Game Theory for Distributed Cooperation in Next Generation Wireless Networks

Next generation wireless networks are bound to go beyond the classical point-to-point or point-to-multipoint communication paradigms of traditional networks such as cellular networks. For instance, next generation networks will witness a highly complex and dynamic environment whereby the nodes can interact and cooperate for improving their performance. In this context, cooperation has emerged as a novel communication paradigm that can yield tremendous performance gains from the physical layer all the way up to the application layer. Consequently, a significant amount of research efforts has been dedicated to studying cooperation in wireless networks. The main research in this area has focused on examining the performance gains that cooperation can entail, in different scenarios. For example, on one hand, it was demonstrated that, by cooperating, a group of single-antenna nodes can exploit the highly acclaimed performance gains of multiple-input-multipleoutput (MIMO) systems in terms of throughput and bit error rate. On the other hand, it has been shown that, by forwarding each others packets, the wireless nodes can increase their throughput and improve the connectivity of the network. However, most of the cooperative systems proposed so far are based on ideal cooperation, e.g., with no cost, and are mostly concerned with studying the benefits from cooperation, while giving little attention to the impact of cooperation on the network’s structure and the users’ behavior. Hence, there is a need for well-designed cooperative algorithms that can reap the numerous gains from cooperation while taking into account the costs for cooperation as well as its impact on the overall network structure and dynamics. Designing such efficient cooperation algorithms faces numerous challenges. First, cooperation entails various costs, such as power, that can limit its benefits or even impair the users’ performance. Second, wireless network users tend to be selfish in nature. Therefore, deriving a fair and practical cooperation algorithm where the decision to cooperate does not degrade the performance of any of the cooperating users is a challenging