TY - GEN
T1 - Deep Reinforcement Learning for Interference Suppression in RIS-Aided High-Speed Railway Networks
AU - Xu, Jianpeng
AU - Ai, Bo
AU - Quek, Tony Q.S.
AU - Liuc, Yupei
N1 - Funding Information:
This work was supported in part by the National Key Research and Development Program under Grant 2021YFB3901302; in part by the Royal Society Newton Advanced Fellowship under Grant NA191006; in part by the NSFC under Grant U1834210, 61725101, and 61961130391; in part by the Natural Science Foundation of Jiangsu Province Major Project under Grant BK20212002; in part by the Major Projects of the Beijing Municipal Science and Technology Commission under Grant Z181100003218010; in part by the Scholarship from the China Scholarship Council under Grant 202007090172; in part by the State Key Lab of Rail Traffic Control and Safety under Grant RCS2020ZT010, RCS2019ZZ007; and in part by the Open Research Fund from the Shenzhen Research Institute of Big Data under Grant 20190RF01006.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
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U2 - 10.1109/ICCWorkshops53468.2022.9814619
DO - 10.1109/ICCWorkshops53468.2022.9814619
M3 - Conference contribution
AN - SCOPUS:85134764817
T3 - 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
SP - 337
EP - 342
BT - 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022
Y2 - 16 May 2022 through 20 May 2022
ER -