📝 SummarXiv

Abstract

Reconfigurable intelligent surfaces (RIS) and flexible intelligent metasurfaces (FIM) have been widely adopted in multi-user wireless communication systems to enhance channel quality through simultaneous transmission and reflection of signals and three-dimensional reconfiguration of antennas. In this paper, we propose a novel system architecture that integrates the benefits of both technologies by deploying an FIM antenna at the base station (BS) and a simultaneously transmitting and reflecting beyond diagonal RIS (STAR-BD-RIS) along the transmission path to ensure sufficient received power for single-antenna users. The objective is to maximize the achievable sum rate considering the short block length by jointly optimizing the FIM surface configuration, the transmit beamforming vector, and STAR-BD-RIS phase shift matrix subject to practical constraints including minimum signal-to-interference-plus-noise ratio (SINR), power limitations, FIM constraint, and the STAR-BD-RIS phase-shift matrix. To solve the resulting non-convex optimization problem, we develop a learning-based approach that incorporates meta-learning into the twin delayed deep deterministic policy gradient (TD3) algorithm, referred to as Meta-TD3. The simulation results demonstrate that the proposed hybrid system outperforms conventional configurations employing either FIM or RIS alone, while the Meta-TD3 algorithm achieves superior performance compared to classic learning techniques.