Intelligent Resource Allocation for Train-to-Train Communication: A Multi-Agent Deep Reinforcement Learning Approach

Abstract

The application of train-to-train (T2T) communication in urban rail transit is expected to simplify system structure, reduce maintenance costs, and improve operational efficiency. In particular, train-to-wayside (T2W) communication coexist with T2T communication in the train control system based on T2T communication. To make full use of limited spectrum resources, frequency reuse is adopted as an efficient technique, but it brings the co-channel interference unfortunately, which affects the quality of service (QoS) for T2T and T2W users. In this paper, we propose a multi-agent deep reinforcement learning (MADRL) based autonomous channel selection and transmission power selection algorithm for T2T communication to reduce the co-channel interference. Specifically, each agent interacts with the environment and selects actions to implement a distributed resource allocation mechanism independently, adopting asynchronous updates to avoid different agents choosing the same sub-band. Simulation results show the superiority of our proposed algorithm: compared with the existing resource allocation schemes for T2T communication, the system throughput and the successful transmission probability of T2T links are greatly improved.