Semi Dynamic Parameter Tuning for Optimized Opportunistic Spectrum Access

Opportunistic spectrum access (OSA) is a hot topic in cognitive radio context. The main challenge of the OSA is to define improved spectral usage schemes, through the utilization of frequency holes in licensed bands. The multi armed bandit (MAB) is a reinforcement learning technique that can provide the secondary user with the adequate rules, in order to perform simultaneously 1) the exploitation of its external environment and 2) the exploration of the accumulated knowledge by transmitting in the identified white spaces. However, the MAB is sensitive to the non-stationarity of the channels' statistical characteristics. Thus, in this paper, we address an offline sensitivity study to optimize the parameter tuning in the used MAB allocation strategies. Also, we propose a semi dynamic parameter tuning scheme to achieve an online update of the MAB parameters. This adaptive MAB solution enhances the performance of the secondary user in dynamic environments.