Applications of Game Theory and Microeconomics in Cognitive Radio and Femtocell Networks
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Cognitive radio networks have recently been proposed as a promising approach to overcome the serious problem of spectrum scarcity. Other emerging concept for innovative spectrum utilization is femtocells. Femtocells are low-power and short-range wireless access points installed by the end-user in residential or enterprise environments. A common feature of cognitive radio and femtocells is their two-tier nature involving primary and secondary users (PUs, SUs). While this new paradigm enables innovative alternatives to conventional spectrum management and utilization, it also brings its own technical challenges. A main challenge in cognitive radio is the design of efficient resource (spectrum) trading methods. Game and microeconomics theories provide tools for studying the strategic interactions through rationality and economic benefits between PUs and SUs for effective resource allocation. In this thesis, we investigate some efficient game theoretic and microeconomic approaches to address spectrum trading in cognitive networks. We propose two auction frameworks for shared and exclusive use models. In the first auction mechanism, we consider the shared used model in cognitive radio networks and design a spectrum trading method to maximize the total satisfaction of the SUs and revenue of the Wireless Service Provider (WSP). In the second auction mechanism, we investigate spectrum trading via auction approach for exclusive usage spectrum access model in cognitive radio networks. We consider a realistic valuation function and propose an efficient concurrent Vickrey-Clarke-Grove (VCG) mechanism for non-identical channel allocation among r-minded bidders in two different cases. The realization of cognitive radio networks in practice requires the development of effective spectrum sensing methods. A fundamental question is how much time to allocate for sensing purposes. In the literature on cognitive radio, it is commonly assumed that fixed time durations are assigned for spectrum sensing and data transmission. It is however possible to improve the network performance by finding the best tradeoff between sensing time and throughput. In this thesis, we derive an expression for the total average throughput of the SUs over time-varying fading channels. Then we maximize the total average throughput in terms of sensing time and the number of SUs assigned to cooperatively sense each channel. For practical implementation, we propose a dynamical programming algorithm for joint optimization of sensing time and the number of cooperating SUs for sensing purpose. Simulation results demonstrate that significant improvement in the throughput of SUs is achieved in the case of joint optimization. In the last part of the thesis, we further address the challenge of pricing in oligopoly market for open access femtocell networks. We propose dynamic pricing schemes based on microeconomic and game theoretic approaches such as market equilibrium, Bertrand game, multiple-leader-multiple-follower Stackelberg game. Based on our approaches, the per unit price of spectrum can be determined dynamically and mobile service providers can gain more revenue than fixed pricing scheme. Our proposed methods also provide residential customers more incentives and satisfaction to participate in open access model.