TY - GEN
T1 - Dual-Functional Waveform Design for STAR-RIS Aided ISAC via Deep Reinforcement Learning
AU - Zhang, Jifa
AU - Gong, Shiqi
AU - Lu, Weidang
AU - Xing, Chengwen
AU - Zhao, Nan
AU - Ng, Derrick Wing Kwan
AU - Niyato, Dusit
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Integrated sensing and communication (ISAC) technology effectively enables spectrum and hardware sharing between radar and communication. This paper investigates the dual-functional (DF) constant modulus waveform design for simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS)-aided ISAC, in which the channel information can be used as semantic information. To investigate the performance trade-off, the weighted sum of multi-user interference (MUI) energy and waveform discrepancies is minimized via jointly optimizing the transmit waveform and the reflection and transmission coefficient matrices at STAR-RIS. Furthermore, a practical case of coupled phase shifts at STARRIS is investigated. We first formulate the optimization problem as a Markov decision process, employing a twin delayed deep deterministic policy gradient (TD3)-based deep reinforcement learning approach to address it. Simulation results verify the effectiveness of the proposed scheme.
AB - Integrated sensing and communication (ISAC) technology effectively enables spectrum and hardware sharing between radar and communication. This paper investigates the dual-functional (DF) constant modulus waveform design for simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS)-aided ISAC, in which the channel information can be used as semantic information. To investigate the performance trade-off, the weighted sum of multi-user interference (MUI) energy and waveform discrepancies is minimized via jointly optimizing the transmit waveform and the reflection and transmission coefficient matrices at STAR-RIS. Furthermore, a practical case of coupled phase shifts at STARRIS is investigated. We first formulate the optimization problem as a Markov decision process, employing a twin delayed deep deterministic policy gradient (TD3)-based deep reinforcement learning approach to address it. Simulation results verify the effectiveness of the proposed scheme.
KW - Deep reinforcement learning
KW - integrated sensing and communication
KW - STAR-RIS
KW - waveform design
UR - http://www.scopus.com/inward/record.url?scp=85206970347&partnerID=8YFLogxK
U2 - 10.1109/PIMRC59610.2024.10817375
DO - 10.1109/PIMRC59610.2024.10817375
M3 - Conference contribution
AN - SCOPUS:85206970347
T3 - IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
BT - 2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024
Y2 - 2 September 2024 through 5 September 2024
ER -
