Spectrum Management in High-Speed Railway Cooperative Cognitive Radio Network Based on Multi-agent Reinforcement Learning

As the high-speed railway industry matures, higher requirements are put forward for the railway wireless communication, and the demand for spectrum resources is also increasing gradually. When the train is moving at a high speed, it will bring about the frequent handover of wireless communication networks, which will lead to the deterioration of wireless communication quality and even the dropping of calls. Therefore, based on the Cognitive Radio, we established the cognitive base station model to enable the base station to have cognitive functions in this paper. We also proposed a multiple base station cooperative reinforcement learning to achieve dynamic spectrum management. Cognitive base station can select the optimal channel for communication services by fusing interaction information between different cognitive base stations, thus reducing the failure of handover when the train crosses the cells. The experimental results showed that the proposed algorithm can improve the utilization of spectrum resources effectively.