URLLC Service for Beyond Diagonal RIS-Enabled ISAC Systems: A Meta-PPO Algorithm

This letter investigates rate-splitting multiple access (RSMA)-based short-packet downlink transmission in an integrated sensing and communication (ISAC) system under channel uncertainty. Specifically, a base station (BS) provides ultra-reliable low-latency communication (URLLC) services to multiple users with the aid of a beyond-diagonal reconfigurable intelligent surface (BD-RIS), while simultaneously detecting a target in the presence of clutter. The objective is to maximize the sum rate by jointly optimizing transmit beamforming, common rate allocation, and BD-RIS reflection beamforming. To address the optimization problem, we propose a Meta proximal policy optimization (Meta-PPO) algorithm, which is a fusion of Meta-learning techniques and the PPO algorithm. Numerical results demonstrate that the proposed BD-RIS-based model outperforms the conventional RIS scheme, while the proposed Meta-PPO algorithm achieves faster convergence and superior performance compared with other deep reinforcement learning (DRL)-based methods.