Distributed Sequential Decision in Rail Transit Cognitive Radio

Abstract

Wireless communication is an important part of key technology in modern rail transit. The rapid trajectory of the train makes the wireless spectrum of rail transit environment instable, discontinuous and unpredictable. These uncertainties, coupled with the inherent scarcity of the spectrum, result in inefficient wireless communications for rail transit. In this paper, We first formulate spectrum management of railway cognitive radio as the distributed sequential decision problem. By using reinforcement learning and agent theory, we propose the cognitive base station model. Furthermore, a multi-cognitive-base-station cascade collaboration algorithm is described according to the characteristics of the chain distribution and cascade operation of base stations along the track. Finally, this paper evaluates the communication performance of test scenarios and proves that the system can significantly improve the probability of successful transmission and greatly reduce the number of wireless channel switching. This cognitive base station multi-agent system scheme provides a new idea for realizing the cognitive radio of the rail transit and comprehensively solving the problem of low efficiency of the wireless spectrum. The paper is also a typical case of artificial intelligence applied in the field of signal processing.