This paper proposes a novel channel allocation scheme based on the replica exchange Monte Carlo method (REMCMC). Some distributed channel allocation schemes in the literature formulate the channel allocation problem as a potential game, in which the unilateral improvement dynamics is guaranteed to converge to a Nash equilibrium. In general, spatial adaptive play (SAP), which is one of the representative learning algorithms in the potential game-based approach, can reach an optimal Nash equilibrium stochastically. However, this is inefficient for the channel allocation and SAP tends to be stuck in a sub-optimal Nash equilibrium in a limited time. To assist in finding the optimal Nash equilibrium for this kind of channel allocation problem, we apply the REMCMC to the existing potential game-based channel allocation. We show that SAP can be considered as a sampling process of the Boltzmann- Gibbs distribution and sampling methods can be utilized. We evaluated the proposed algorithm through simulations and the results show that the proposed algorithm can find the optimal Nash equilibrium quickly.


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    Title :

    Replica Exchange Spatial Adaptive Play for Channel Allocation in Cognitive Radio Networks


    Contributors:


    Publication date :

    2019-04-01


    Size :

    407328 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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