Managing Single or Multi-Users Channel Allocation for the Priority Cognitive Access

This manuscript investigates the problem of the Multi-Armed Bandit (MAB) in the context of the Opportunistic Spectrum Access (OSA) case with priority management (e.g. military applications). The main aim of a Secondary User (SU) in OSA is to increase his transmission throughput by seeking the best channel with the highest vacancy probability. In this manuscript, we propose a novel MAB algorithm called ϵ -UCB in order to enhance the spectrum learning of a SU and decrease the regret, i.e. the loss of reward due to the selection of worst channels. We analytically prove, and corroborate with simulations, that the regret of the proposed algorithm has a logarithmic behavior. So, after a finite number of time slots, the SU can estimate the vacancy probability of channels in order to target the best one for transmitting. Hereinafter, we extend ϵ -UCB to consider multiple priority users, where a SU can selfishly estimate and access the channels according to his prior rank. The simulation results show the superiority of the proposed algorithm for a single or multi-user cases compared to the well-known MAB algorithms.