DRL-Based Joint Beamforming and Surface Shape Optimization for Flexible Intelligent Metasurface-Aided ISAC Systems
It addresses the problem of improving sensing performance in ISAC systems with flexible metasurfaces, offering a DRL-based solution that outperforms existing rigid arrays.
This paper proposes a DRL-based joint optimization of beamforming and flexible intelligent metasurface shape to minimize the Cramér-Rao bound under QoS constraints in ISAC systems, achieving lower CRB than rigid arrays.
Integrated sensing and communication (ISAC) unifies high-precision sensing and wireless data transmission. In this paper, we investigate the design of ISAC systems enabled by flexible intelligent metasurface (FIM) and aim to minimize the Cramér-Rao bound (CRB) with quality of service (QoS) constraints using deep reinforcement learning (DRL). Specifically, we formulate the joint design of beamforming matrix and FIMs surface shape to reduce the CRB subject to transmit power, QoS and the FIMs surface shape constraints. However, the non-convex formulation makes optimization problem difficult to solve. To tackle this issue, we develop a deep deterministic policy gradient (DDPG) actor critic DRL scheme for the joint design, guided by a constraint aware reward to progressively improve sensing performance. Numerical results demonstrate that jointly optimizing the beamforming matrix and the FIMs surface shape substantially decreases CRB while ensuring communication quality compared with existing rigid arrays.