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This paper introduces a machine learning based collaborative multi-bandspectrum sensing policy for cognitive radios. The proposed sensing policyguides secondary users to focus the search of unused radio spectrum to thosefrequencies that persistently provide them high data rate. The proposed policyis based on machine learning, which makes it adaptive with the temporally andspatially varying radio spectrum. Furthermore, there is no need for dynamicmodeling of the primary activity since it is implicitly learned over time.Energy efficiency is achieved by minimizing the number of assigned sensors pereach subband under a constraint on miss detection probability. It is importantto control the missed detections because they cause collisions with primarytransmissions and lead to retransmissions at both the primary and secondaryuser. Simulations show that the proposed machine learning based sensing policyimproves the overall throughput of the secondary network and improves theenergy efficiency while controlling the miss detection probability.

Reinforcement learning based sensing policy optimization for energy efficient cognitive radio networks