Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach

This paper proposes a novel network architecture for 6G indoor communication that combines multiple access points (APs) and reconfigurable intelligent surfaces (STAR-RISs) for enhanced performance and interference mitigation. The authors employ a decomposition approach, using a many-to-one matching algorithm for user assignment, K-means clustering for AP grouping, and multi-agent deep reinforcement learning (MADRL) for STAR-RIS control. The focus is on optimizing user assignment, beamforming, and phase control while considering power consumption and real-time configuration. The study highlights the potential of STAR-RISs in improving network utility, capacity, and energy efficiency, with simulations showing significant improvements over conventional methods. The research also explores the use of convex approximation in MADRL to accelerate learning and addresses challenges in channel modeling and optimization. Overall, the paper contributes to the understanding and optimization of RIS-assisted wireless networks for future 6G systems.