Resource Allocation in Uplink Multi STAR-RIS-aided NOMA System via Meta-Learning
CoRR(2024)
摘要
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.
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