Dual-Functional Waveform Design for STAR-RIS Aided ISAC via Deep Reinforcement Learning

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.