Realizing Railway Cognitive Radio: A Reinforcement Base-Station Multi-Agent Model

Wireless communication plays a vital role in the operations of modern rail transportation. The rapid motion characteristics of the train make the wireless spectrum environment unstable and discontinuous. These uncertainties, coupled with the inherent scarcity of the spectrum, lead to inefficiencies in railroad wireless communications. The application of cognitive radio is becoming a cutting-edge research field in railway wireless communication. This paper first analyzes the physical infrastructure of the railway wireless communication network and determines base station as the key communication node in the railway environment, which can implement the cognitive radio technology. Reinforcement learning and agent theory are then used to construct a cognitive base-station model which is suitable for the railway wireless environment. Furthermore, according to the characteristics of the chain-like distribution and cascade operation of the cognitive base stations along the railway, the reinforcement base-station multi-agent system model is proposed, and the unique $Dual\,\,\varepsilon - greedy$ mechanism is used to drive the learning of multi-agent system to avoid local optimization. Our experimental results prove that the model can significantly improve the probability of successful data transmission in the railway wireless communication network, and greatly reduce the number of wireless channel switching. In addition, the effect of $Dual\,\,\varepsilon - greedy$ mechanism on communication performance is discussed. This reinforcement base-station multi-agent model in this paper provides a new idea for realizing the railway cognitive radio and comprehensively solves the problem of low spectrum efficiency of cognitive radio in rail transit.