Learning mechanisms for achieving context awareness and intelligence in Cognitive Radio networks

Proyiding that licensed or Primary Users (PUs) are oblivious to the presence of unlicensed or Secondary Users (SUs), Cognitive Radio (CR) enables the SUs to use underutilized licensed spectrum (or white spaces) opportunistically and temporarily conditional on the interference to the PUs being below an acceptable level. Context awareness and intelligence enable the SU to sense for and use the underutilized licensed spectrum in an efficient manner. This paper investigates various learning mechanisms for achieving context awareness and intelligence with respect to Dynamic Channel Selection (DCS) in CR networks. The learning mechanisms are Adaptation (Adapt), Window (Win), Adaptation-Window (AdaptWin), and Reinforcement Learning (RL). The DCS scheme helps SU base station to select channel adaptively for data transmission to its SU host in static and mobile centralized CR networks. The purpose is to enhance quality of service, particularly throughput and delay (in terms of number of channel switches), in the presence of channel heterogeneity. Our contribution is to investigate simple and yet pragmatic learning mechanisms for CR networks. Simulation results reveal that RL, AdaptWin and Win achieve approximately similar and the best possible network performance, followed by Adapt, and finally Random, which does not apply learning and serves as baseline.