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Simultaneously transmitting and reflecting reconfigurable intelligent surface(STAR-RIS) is a novel technology which enables the full-space coverage. In thisletter, a multi-active STAR-RIS-aided system using non-orthogonal multipleaccess in an uplink transmission is considered, where the second-orderreflections among multiple active STAR-RISs assist the transmission from thesingle-antenna users to the multi-antenna base station. Specifically, the totalsum rate maximization problem is solved by jointly optimizing the activebeamforming, power allocation, transmission and reflection beamforming at theactive STAR-RISs, and user-active STAR-RIS assignment. To solve the non-convexoptimization problem, a novel deep reinforcement learning algorithm is proposedwhich integrates Meta-learning and deep deterministic policy gradient (DDPG),denoted by Meta-DDPG. Numerical results reveal that our proposed Meta-DDPGalgorithm outperforms the DDPG algorithm with $19\%$ improvement, whilesecond-order reflections among multi-active STAR-RISs provide $74.1\%$enhancement in the total data rate.

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