A Bayesian Learning Automata-Based Distributed Channel Selection Scheme for Cognitive Radio Networks
We consider a scenario where multiple Secondary Users (SUs) operate within a Cognitive Radio Network (CRN) which involves a set of channels, where each channel is associated with a Primary User (PU). We investigate two channel access strategies for SU transmissions. In the first strategy, the SUs will send a packet directly without operating Carrier Sensing Medium Access/Collision Avoidance (CSMA/CA) whenever a PU is absent in the selected channel. In the second strategy, the SUs implement CSMA/CA to further reduce the probability of collisions among co-channel SUs. For each strategy, the channel selection problem is formulated and demonstrated to be a so-called “Potential” game, and a Bayesian Learning Automata (BLA) has been incorporated into each SU so to play the game in such a manner that the SU can adapt itself to the environment. The performance of the BLA in this application is evaluated through rigorous simulations. These simulation results illustrate the convergence of the SUs to the global optimum in the first strategy, and to a Nash Equilibrium (NE) point in the second.
KeywordsCognitive radio Channel access Multiple users Potential game BLA
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