Resource Allocation in Uplink Multi STAR-RIS-aided NOMA System via Meta-Learning

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is a novel technology which enables the full-space coverage by splitting the incident signal into reflected and transmitted signals. In this letter, a multi STAR-RIS-aided system using non-orthogonal multiple access (NOMA) in an uplink transmission is considered, where the multi-order reflections among multiple STAR-RISs assist the transmission from the single-antenna users to the multi-antenna base station (BS). Specifically, the total sum rate maximization problem is solved by jointly optimizing the active beamforming, power allocation, transmission and reflection beamforming at the STAR-RIS, and user-STAR-RIS association indicator. To solve the non-convex optimization problem, a novel deep reinforcement learning algorithm is proposed which is the combination of meta-learning and deep deterministic policy gradient (DDPG), namely Meta-DDPG. Numerical results demonstrate that the proposed Meta-DDPG algorithm outperforms the conventional DDPG algorithm.