Joint User-Centric and System-Centric Channel Allocation in Cognitive Radio Networks: A Game-Theoretic Perspective.
DOI: 10.1109/CNSR.2009.69 Conference: 7th Annual Conference on Communication Networks and Services Research, CNSR 2009, 11-13 May 2009, Moncton, Canada
In this paper, we consider the channel allocation for selfish secondary user (SU) in cognitive radio networks. In general, there are two allocation approaches for our scenario. The user-centric allocation approach aims to maximize the revenues of individual SUs and the system-centric allocation approach attempts to maximize the system revenue. In this paper, we focus on the joint user-centric and system-centric allocation approach and aim to investigate two fundamental issues below: 1) Is there an allocation profile which maximizes the revenues of the SU sand the system? 2) How to achieve this desirable allocation if it exists? We use the game theory approach to investigate these issues. We model the problem of the channel allocation as an ordinal potential game and prove the existence of the desirable allocation profile. Furthermore, we propose a simple protocol to achieve it. Finally, considering the practical issues, we propose a self-enforcing truth-telling method and a best response based algorithm which uses only local information.
Article: Game Theory for Wireless EngineersSynthesis Lectures on Communications 01/2006; 1(1):1-86. DOI:10.2200/S00014ED1V01Y200508COM001
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ABSTRACT: Cognitive radio has been proposed as a novel approach for improving the utilization of the precious limited radio resources by dynamically accessing the spectrum. One of the major design challenges is to coordinate and cooperate in accessing the spectrum opportunistically among multiple distributive users with only local information. In this paper, we propose a game theoretical approach with a new solution concept, the correlated equilibrium, which is better compared to the non-cooperative Nash equilibrium in terms of spectrum utilization efficiency and fairness among the distributive users. To achieve this correlated equilibrium, we construct an adaptive algorithm based on no-regret learning that guarantees convergence. From the simulation results, the optimal correlated equilibria achieve better fairness and 5% ~15% performance gain, compared to the Nash equilibria.IEEE Wireless Communications and Networking Conference, WCNC 2007, Hong Kong, China, 11-15 March, 2007; 01/2007
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ABSTRACT: In this work, we propose a game theoretic framework to analyze the behavior of cognitive radios for distributed adaptive channel allocation. We define two different objective functions for the spectrum sharing games, which capture the utility of selfish users and cooperative users, respectively. Based on the utility definition for cooperative users, we show that the channel allocation problem can be formulated as a potential game, and thus converges to a deterministic channel allocation Nash equilibrium point. Alternatively, a no-regret learning implementation is proposed for both scenarios and it is shown to have similar performance with the potential game when cooperation is enforced, but with a higher variability across users. The no-regret learning formulation is particularly useful to accommodate selfish users. Non-cooperative learning games have the advantage of a very low overhead for information exchange in the network. We show that cooperation based spectrum sharing etiquette improves the overall network performance at the expense of an increased overhead required for information exchangeNew Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. 2005 First IEEE International Symposium on; 12/2005
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