Channel selection in Cognitive Radio Networks: A Switchable Bayesian Learning Automata approach

Abstract

We consider the problem of a user operating within a Cognitive Radio Network (CRN) which involves N channels each associated with a Primary User (PU). The problem consists of allocating a channel which, at any given time instant is not being used by a PU, to a Secondary User (SU). Within our study, we assume that a SU is allowed to perform “channel switching”, i.e., to choose an alternate channel S times (where S +1 ≤ N) if the previous choice does not lead to a channel which is vacant. The paper first presents a formal probabilistic model for the problem itself, referred to as the Formal Secondary Channel Selection (FSCS) problem, and the characteristics of the FSCS are then analyzed. Thereafter, the paper proposes a fascinating solution to the FSCS problem by invoking the recently devised Bayesian Learning Automaton (BLA). The crucial advantage of the BLA is that unlike traditional Learning Automata (LA), it does not involve an action probability vector, but rather relies on “sampling” as per the a posteriori Bayesian estimates of the channel occupation probabilities. However, rather than utilize the BLA in the form that was earlier proposed, we shall extend it to the so-called Switchable Bayesian Learning Automaton (SBLA), which, indeed, attains the optimal solution in the overall composite action space. Apart from proposing the solution, the paper also contains detailed simulation results which demonstrate the power of the solution proposed.