Resource Allocation for UAV-aided Communication in High-Speed Railway Systems: A Multi-Agent Reinforcement Learning Approach

• 1、Beijing Key Laboratory of Work Safety Intelligent Monitoring (Beijing University of Posts and Telecommunications), School of Electronic Engineering, Beijing University of Posts and Telecommunications, 100876

Abstract：The past decades have witnessed the rapid developments of high-speed railways (HSRs) communications. To provide seamless communication services between high-speed trains, both mobile edge computing (MEC) servers and UAVs are integrated into HSRs to provide on-demand resource access. However, the sensitive delay requirements of high-speed services pose significant challenges to the resource allocation in HSRs. This paper will formulate the UAV-aided resource allocation in high-speed railways (HSRs) as a distributed optimization problem to optimize the resource utilization while minimizing the path blocking probability. To address this problem, a multi-agent deep deterministic policy gradient (multi-agent DDPG) approach is proposed. The MEC servers are taken as the agents to make resource allocation decisions in the training phase. The simulation shows demonstrate that multi-agent DDPG outperforms the traditional single-agent method. The proposed multi-agent DDPG-based resource allocation algorithm can achieve satisfactory performance.?????

keywords： High-speed railway deep reinforcement learning unmanned aerial vehicle resource allocation multi-agent DDPG.

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