Resource Allocation and Beamforming in FIM-Assisted BS and STAR-BD-RIS-Aided NOMA: An AIW-Meta-Learning Approach

This paper investigates a flexible intelligent metasurface (FIM)-enabled wireless communication system that integrates simultaneously transmitting and reflecting beyond diagonal reconfigurable intelligent surfaces (STAR-BD-RIS) with non-orthogonal multiple access (NOMA). The considered system consists of a multi-antenna FIM-assisted base station (BS) supported by dual-sector BD-RIS. The FIM is composed of low-cost radiating elements capable of independent signal transmission and dynamic vertical reconfiguration (morphing). The objective is to maximize energy efficiency (EE) by jointly optimizing the BS beamforming, STAR-BD-RIS configuration, NOMA-related variables, and the FIM surface shape under practical power constraints. Due to the highly non-convex nature of the problem, an adaptive inverse-weighted Meta-Soft Actor-Critic (AIW-Meta-SAC) algorithm is proposed. Unlike conventional Meta-SAC approaches, the proposed method employs an adaptive weighting mechanism to effectively incorporate system constraints into the reward function, thereby improving learning efficiency and convergence behavior. Simulation results demonstrate that the proposed AIW-Meta-SAC significantly outperforms the Meta-DDPG baseline. Furthermore, the FIM-assisted STAR-BD-RIS architecture achieves notable energy efficiency gains compared to conventional benchmark schemes.