Abstract
We consider an integrated sensing and communication (ISAC) system, in which a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assists a base station in transmitting communication signals to mobile users and conducting sensing tasks toward specific targets. We formulate a transmit beamforming and phase shift optimization problem to jointly maximize the total communication data rate and total effective sensing power. The optimization problem is inherently non-convex, making it challenging to find an optimal solution. To tackle this difficulty, we propose a meta soft actorcritic (meta-SAC) algorithm, which is a fusion of the SAC algorithm and meta-learning techniques. Through extensive simulations, we demonstrate that the proposed meta-SAC algorithm outperforms traditional deep reinforcement learning methods, thus showing its potential to enhance the performance of ISAC systems significantly.Abstract
We consider an integrated sensing and communication (ISAC) system, in which a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assists a base station in transmitting communication signals to mobile users and conducting sensing tasks toward specific targets. We formulate a transmit beamforming and phase shift optimization problem to jointly maximize the total communication data rate and total effective sensing power. The optimization problem is inherently non-convex, making it challenging to find an optimal solution. To tackle this difficulty, we propose a meta soft actorcritic (meta-SAC) algorithm, which is a fusion of the SAC algorithm and meta-learning techniques. Through extensive simulations, we demonstrate that the proposed meta-SAC algorithm outperforms traditional deep reinforcement learning methods, thus showing its potential to enhance the performance of ISAC systems significantly
