Time Prediction Based Spectrum Usage Detection in Centralized Cognitive Radio Networks
Cognitive Radio (CR) networks rely on the spectrum sensing function to ensure that there is no interference to the licensed or Primary Users (PUs). Typically, sensing algorithms assume a static PU activity model, i.e., spectrum usage model, which is constant for a given channel and known in advance. This approach fails to capture the dynamic and time-varying behavior of the PUs. In this paper, a spectrum usage detection approach based on time prediction for centralized CR networks is proposed. The proposed approach allows the CR users to learn about the activity of the PUs, and adapt to subsequent changes.