Robust Beamforming Optimization for STAR-RIS Empowered Multi-User RSMA Under Hardware Imperfections and Channel Uncertainty

TL;DR

This paper develops a robust optimization framework for STAR-RIS-assisted multi-user RSMA systems, enhancing spectral efficiency and resilience against hardware impairments and channel uncertainties in future wireless networks.

Contribution

It introduces a joint active and passive beamforming design method considering hardware impairments and channel uncertainties, with an iterative convex optimization algorithm for robust performance.

Findings

Significant performance improvements over benchmark schemes.

Fast convergence of the proposed optimization algorithm.

Enhanced robustness against channel estimation errors.

Abstract

This study explores the synergy between rate-splitting multiple access (RSMA) and simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) as a unified framework to enable ubiquitous, intelligent, and resilient connectivity in future sixth-generation networks, while improving spectral and energy efficiency. Specifically, we investigate a STAR-RIS-assisted multi-user RSMA system and develop an intelligent optimization strategy that jointly designs the transmitter's active beamforming, the common stream rate allocation, and the passive beamforming vectors for the STAR-RIS transmission and reflection regions, considering transceiver hardware impairments and imperfect channel state information (CSI). In addition, system robustness is ensured via a bounded channel estimation error model that captures CSI imperfections and guarantees resilience against worst-case errors. To address the highly non-convex problem, we propose an iterative optimization algorithm that decomposes it into two sub-problems. Firstly, active beamforming vectors for the common and private signals are determined by reformulating the original problem into a convex semi-definite programming (SDP) form using successive convex approximation (SCA) and semi-definite relaxation (SDR). Secondly, passive beamforming vectors are optimized through a convex SDP reformulation by exploiting SCA and SDR techniques. Moreover, when higher-rank solutions arise, Gaussian randomization is applied to obtain rank-one solutions. Numerical simulations demonstrate that the proposed strategy achieves significant performance gains over benchmark schemes and exhibits fast convergence.