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. CR base station selects CR user with the longest sensing time predicted by a mobile model. Each selected mobile CR user uses maximum likelihood estimator (MLE) on the observed ON/OFF period samples to estimate the average busy and idle periods. In addition, CR base station employs mean square error (MSE) to determine when the fine sensing should stop, and exploits the variation of MSE to restart the fine sensing. Simulation results reveal that our proposed method can efficiently and quickly track the dynamics of the PU spectrum usage.