Opportunistic channel access with repetition time diversity and switching cost: a block multi-armed bandit approach

In this paper, we investigate the channel access problem considering switching cost in the block fading channels with unknown information of channel occupation and quality. We formulate this problem as a multi-armed bandit (MAB) problem with the goal of minimizing the outage rate and avoiding frequent channel switching. To achieve this goal, a block based multi-armed bandit (BMAB) learning algorithm is proposed. Furthermore, the BMAB algorithm is extended to cope with the short-term deep channel fading, by exploiting the repetition time diversity (RTD). The regrets of the proposed two algorithms are proved to be logarithmic in time. Performance analysis and simulation results show that the proposed algorithms outperform standard SMAB algorithm in average system outage rate, switching cost and throughput. In addition, the repetition time diversity multi-armed bandit (RTDMAB) algorithm is better than BMAB algorithm in the presence of deep channel fading at the cost of receiving complexity .