Spectrum prediction and channel selection for sensing-based spectrum sharing scheme using online learning techniques

The cognitive radio technology allows secondary user (SU) to share the licensed spectrum by adapting its transmission power in a sensing-based spectrum sharing manner. Reliable spectrum prediction and channel selection could alleviate the processing delays and enhance the spectrum utilization. In this paper, we propose a new strategy for spectrum prediction and channel selection using online machine learning techniques, which consists of three stages: 1) SU utilizes online learning techniques for the regression of received transmit power on different licenced frequency bands; 2) SU predicts the probability of each primary user´s status (busy/idle) based on the power regression results; 3) SU optimizes channel selection in terms of expected ergodic capacities from the prediction outcomes. The proposed strategy can not only save time and energy, but also enhance the throughput of SU. The performance of the proposed strategy is evaluated through extensive simulations.