Abstract
While hybrid-band wireless systems recently gained prominence to achieve high capacity, selecting the best channel in these systems in real-time is still a formidable research challenge that requires further investigations. In this paper, we address this challenge in terms of an optimization problem, which is reformu-lated as a stochastic multi-armed bandit (MAB). Then, we introduce online learning-based solutions to solve the MAB problem for the multi-band/channel selection (MBS). Improved variants of the upper confidence bound (UCB) scheme are investigated and modified to be energy-aware. Hence, we propose Energy-Aware Randomized UCB-MBS (EA-RUCB-MBS) and Energy-Aware Kullback-Leibler UCB-MBS (EA-KLUCB-MBS) methods, which demonstrate near-optimal results. Also, EA-KLUCB-MBS exhibits the fastest convergence, while the convergence of EA-RUCB-MBS is similar to that of the original UCB. Based on extensive simulation results, we evaluate the performance of our proposed algorithms against benchmark MBS schemes including UCB and Thompson sampling (TS).