EFFICIENT TECHNIQUES FOR PERFORMANCE ENHANCEMENT IN INTELLIGENT SURFACE-ENABLED WIRELESS NETWORKS

Abstract

Our study delves into the capabilities of Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) in boosting the performance of uplink-downlink (UL-DL) Non-Orthogonal Multiple Access (NOMA) networks. By segmenting STAR-RIS, we aim to improve NOMA users’ channel gains, enhancing the efficiency of NOMA integration and obviating the necessity for UL power adjustments. We thoroughly examine our approach across two key optimization problems: feasible region and max-min rate (MMR), ensuring compliance with QoS demands for both UL-DL. Our method deploys the independent roles of STAR-RIS partitioning and Base Station (BS) transmission power to derive explicit formulas that reveal the effectiveness of optimal STAR-RIS portion in satisfying UL-QoS needs, while BS power management guarantees DL-QoS satisfaction. The robustness of our analytical conclusions is confirmed through simulation experiments, which underscore the substantial benefits STAR-RIS technology presents to NOMA networks under these varied operational frameworks.