Deep Reinforcement Learning for Interference Suppression in RIS-Aided High-Speed Railway Networks

Jianpeng Xu, Bo Ai, Tony Q.S. Quek, Yupei Liuc

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper investigates the reconfigurable intelligent surface (RIS)-aided high-speed railway (HSR) network, where one RIS is deployed nearby the onboard mobile relay (MR) to suppress the external interference in HSR system. In order to enhance the HSR network capacity against the interference, we formulate an optimization problem for designing the phase shifts at the RIS. Since the HSR environment is time-varying and complicated, the optimization problem is challenging to settle. Inspired by the recent advances of artificial intelligence (AI), we propose a deep reinforcement learning (DRL)-based scheme to design the RIS phase shifts. Simulation results show that 1) deploying the RIS nearby the onboard MR is strongly facilitative of suppressing the interference; 2) the proposed DRL scheme can achieve better capacity than the baseline schemes.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages337-342
Number of pages6
ISBN (Electronic)9781665426718
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022 - Seoul, Korea, Republic of
Duration: 2022 May 162022 May 20

Publication series

Name2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022

Conference

Conference2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
Country/TerritoryKorea, Republic of
CitySeoul
Period22-05-1622-05-20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Signal Processing
  • Control and Optimization

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