Joint System Sum Rate and User Fairness Optimization in STAR-RIS-Assisted Systems

Abstract

Simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is crucial to achieve full-space coverage in next-generation wireless networks. However, optimizing resource allocation in STAR-RIS-assisted systems to balance the system sum rate with user fairness, especially in the presence of imperfect channel state information (CSI), remains a significant challenge. To address this issue, this work investigates resource allocation in an STAR-RIS-assisted multiple-input single-output system under imperfect CSI and proposes a novel method based on the deep reinforcement learning (DRL) framework to solve this problem. Specifically, the DRL framework is utilized to solve the maximization problem of the weighted sum of Jain’s fairness index and the normalized system sum rate, and a segmented training strategy is employed to decouple the complexity of the original joint optimization problem. The simulation results demonstrate that the proposed solution achieves a flexible trade-off between the system sum rate and user fairness. Moreover, it effectively mitigates the performance degradation caused by imperfect CSI, thereby ensuring robust system performance.