A Cooperative Spectrum Sensing With Multi-Agent Reinforcement Learning Approach in Cognitive Radio Networks

Abstract

Cognitive radio networks (CRNs) can greatly improve the temporal and spatial spectrum utilization by identifying and exploring spectrum holes of the licensed primary users (PUs). However, since the occupation of primary channels changes dynamically, a swift and accurate spectrum sensing is crucial especially in the multi-channel multi-secondary users (SUs) environment, where the number of channels is much larger than that of SUs. To improve the sensing accuracy, a cooperative sensing algorithm is proposed in this letter, where multiple SUs can share their spectrum detection results for a more effective spectrum holes search. This letter further employs multi-agent deep deterministic policy gradient (MADDPG) algorithm with the feature of centralized training and decentralized execution to reduce the synchronization and communication overhead caused by the sensing cooperation of SUs. The numerical simulation demonstrates that with the combination of cooperative sensing and multi-agent reinforcement learning, the proposed algorithm can greatly enhance the sensing accuracy in comparison to other non-cooperative learning or centralized learning approaches.