TY - GEN
T1 - DRL-based STAR-RIS-Assisted ISAC Secure Communications
AU - Zhu, Zhengyu
AU - Gong, Mengfei
AU - Chu, Zheng
AU - Xiao, Pei
AU - Sun, Gangcan
AU - Mi, De
AU - He, Ziming
AU - Tong, Fei
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we explore a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-Assisted integrated sensing and communication (ISAC) secure communication system. The average long-Term security rate of the legitimate user (LU) is maximized by jointly designing the receive filters and transmit beamforming of the base station (BS), and the transmitting and reflecting coefficients of STAR-RIS, and in the meantime, guaranteeing the lower bound of echo signal-To-noise ratio (SNR) and the achievable rate of LU constraint. We propose to apply two deep reinforcement learning (DRL) algorithms to solve the complex non-convex problem and maximize the long-Term benefits of the system by optimizing the BS beamforming and STAR-RIS phase shifts. The simulation results thoroughly evaluate the performance of two DRL algorithms and demonstrate that STAR-RIS outperforms the conventional reconfigurable intelligent surface (RIS) in comparison with two benchmarks.
AB - In this paper, we explore a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-Assisted integrated sensing and communication (ISAC) secure communication system. The average long-Term security rate of the legitimate user (LU) is maximized by jointly designing the receive filters and transmit beamforming of the base station (BS), and the transmitting and reflecting coefficients of STAR-RIS, and in the meantime, guaranteeing the lower bound of echo signal-To-noise ratio (SNR) and the achievable rate of LU constraint. We propose to apply two deep reinforcement learning (DRL) algorithms to solve the complex non-convex problem and maximize the long-Term benefits of the system by optimizing the BS beamforming and STAR-RIS phase shifts. The simulation results thoroughly evaluate the performance of two DRL algorithms and demonstrate that STAR-RIS outperforms the conventional reconfigurable intelligent surface (RIS) in comparison with two benchmarks.
KW - deep reinforcement learning (DRL)
KW - integrated sensing and communication (ISAC)
KW - secrecy rate
KW - simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)
UR - http://www.scopus.com/inward/record.url?scp=85169038223&partnerID=8YFLogxK
U2 - 10.1109/Ucom59132.2023.10257639
DO - 10.1109/Ucom59132.2023.10257639
M3 - Conference contribution
AN - SCOPUS:85169038223
T3 - 2023 International Conference on Ubiquitous Communication, Ucom 2023
SP - 127
EP - 132
BT - 2023 International Conference on Ubiquitous Communication, Ucom 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Ubiquitous Communication, Ucom 2023
Y2 - 7 July 2023 through 9 July 2023
ER -