Let Cognitive Radios Imitate: Imitation-based Spectrum Access for Cognitive Radio Networks

In this paper, we tackle the problem of opportunistic spectrum access in large-scale cognitive radio networks, where the unlicensed Secondary Users (SU) access the frequency channels partially occupied by the licensed Primary Users (PU). Each channel is characterized by an availability probability unknown to the SUs. We apply evolutionary game theory to model the spectrum access problem and develop distributed spectrum access policies based on imitation, a behavior rule widely applied in human societies consisting of imitating successful behavior. We first develop two imitation-based spectrum access policies based on the basic Proportional Imitation (PI) rule and the more advanced Double Imitation (DI) rule given that a SU can imitate any other SUs. We then adapt the proposed policies to a more practical scenario where a SU can only imitate the other SUs operating on the same channel. A systematic theoretical analysis is presented for both scenarios on the induced imitation dynamics and the convergence properties of the proposed policies to an imitation-stable equilibrium, which is also the $\epsilon$-optimum of the system. Simple, natural and incentive-compatible, the proposed imitation-based spectrum access policies can be implemented distributedly based on solely local interactions and thus is especially suited in decentralized adaptive learning environments as cognitive radio networks

Proportional and Double Imitation Rules for Spectrum Access in Cognitive Radio Networks

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Retrospective Spectrum Access Protocol: A Completely Uncoupled Learning Algorithm for Cognitive Networks

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Self-Imitation in Cognitive Radio Networks

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Imitation-based Spectrum Access Policy for CSMA/CA-based Cognitive Radio Networks

International audienceIn this paper, we tackle the problem of opportunistic spectrum access in cognitive radio networks where a number of unlicensed Secondary Users (SU) operating on the standard CSMA/CA protocol access a number of frequency channels partially occupied by licensed Primary Users (PU). We apply evolutionary game theory to model the spectrum access problem and derive distributed mechanisms to converge to the Nash equilibrium. To this end, we combine a payoff computation methodology, relying on the estimation on the number of SUs on the same channel, with the channel access policy derived by the evolutionary game model. The conducted numerical analysis shows that a fast convergence is achieved and the proposed mechanisms are robust against errors in payoff computation

Imitation-based spectrum access policy for CSMA/CA-based cognitive radio networks

International audienc