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Resource Allocation for UAV-aided Communication in High-Speed Railway Systems: A Multi-Agent Reinforcement Learning Approach
Zhou Lanlan,Yu Jianguo *
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;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
*Correspondence author
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Funding: National Natural Science Foundations of China （No.61821001）, National Natural Science Foundations of China （No.61531007）
Opened online: 5 March 2021
Accepted by: none
Citation: Zhou Lanlan,Yu Jianguo.Resource Allocation for UAV-aided Communication in High-Speed Railway Systems: A Multi-Agent Reinforcement Learning Approach[OL]. [ 5 March 2021] http://en.paper.edu.cn/en_releasepaper/content/4753869
 
 
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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