Reinforcement learning-based cooperative sensing in cognitive radio ad hoc networks

In cognitive radio networks, spectrum sensing is a fundamental function for detecting the presence of primary users in licensed frequency bands. Due to multipath fading and shadowing, the performance of detection may be considerably compromised. To improve the detection probability, cooperative sensing is an effective approach for secondary users to cooperate and combat channel impairments. This approach, however, incurs overhead such as sensing delay for reporting local decisions and the increase of control traffic in the network. In this paper, a reinforcement learning-based cooperative sensing method is proposed to address the cooperation overhead problem. By using the proposed cooperative sensing model, the secondary user learns to (i) find the optimal set of cooperating neighbors with minimum control traffic, (ii) minimize the overall cooperative sensing delay, (iii) select independent users for cooperation under correlated shadowing, and (iv) improve the energy efficiency for cooperative sensing. The simulation results show that the proposed reinforcement learning-based cooperative sensing method reduces the overhead of cooperative sensing while effectively improving the detection performance to combat correlated shadowing.