Predictive spectrum sensing strategy based on reinforcement learning

In this paper, we consider a cognitive radio (CR) system with a single secondary user (SU) and multiple licensed channels. The SU requests a fixed number of licensed channels and must sense the licensed channels one by one before transmission. By leveraging prediction based on correlation between the licensed channels, we propose a novel spectrum sensing strategy, to decide which channel is the best choice to sense in order to reduce the sensing time overhead and further improve the SU´s achievable throughput. Since the correlation coefficients between the licensed channels cannot be exactly known in advance, the spectrum sensing strategy is designed based on the model-free reinforcement learning (RL). The experimental results show that the proposed spectrum sensing strategy based on reinforcement learning converges and outperforms random sensing strategy in terms of long-term statistics.