This study investigates the synergy between ratesplitting multiple access (RSMA) and simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) as a unified framework to realize ubiquitous, intelligent, and resilient connectivity in future sixth-generation networks, while enhancing both spectral and energy efficiency. Specifically, in the STAR-RIS-assisted multi-user RSMA network under consideration, we develop an intelligent optimization strategy that jointly designs the active beamforming at the transmitter, the allocated transmission rate for the common stream, and the passive beamforming vectors for both transmission and reflection regions of the STAR-RIS, while accounting for transceiver hardware impairments and imperfect channel state information (CSI). In addition, system robustness is ensured by incorporating a bounded channel estimation error model that rigorously reflects CSI imperfections and ensures resilience against worst-case estimation errors. To tackle the highly non-convex problem, we propose an intelligent optimization algorithm that decouples the original problem into two sub-problems, which are then solved iteratively. Firstly, the active beamforming vectors for both the common and private signals are obtained by reformulating the original non-convex problem into a tractable convex semi-definite programming (SDP) framework, leveraging successive convex approximation (SCA) and semi-definite relaxation (SDR) for enhanced computational efficiency. Secondly, the passive beamforming vectors for the transmission and reflection regions of the STAR-RIS are optimized through a convex SDP reformulation by exploiting SCA and SDR techniques. Additionally, when the resulting active or passive beamforming solutions are of higher rank, Gaussian randomization is employed to construct rank-one solutions. Finally, the effectiveness of the proposed optimization strategy is demonstrated through numerical simulations, which reveal significant performance gains over benchmark schemes and confirm rapid convergence.

This work explores the integration of rate-splitting multiple access (RSMA), simultaneous wireless information and power transfer (SWIPT), and beyond-diagonal reconfigurable intelligent surface (BD-RIS) to enhance the spectral-efficiency, energy efficiency, coverage, and connectivity of future sixth-generation (6G) communication networks. Specifically, with a multiuser BD-RIS-empowered RSMA-SWIPT system, we jointly optimize the transmit precoding vectors, the common rate proportion of users, the power-splitting ratios, and scattering matrix of the BD-RIS, under the assumption of imperfect channel state information (CSI). Additionally, to better capture practical hardware behavior, we incorporate a nonlinear energy harvesting model and ensure that the resulting system satisfies all energy harvesting constraints. In the considered system, we design a robust optimization framework to maximize the system sum-rate, while explicitly accounting for the worst-case impact of CSI uncertainties. To tackle the inherent non-convexity of the problem, we introduce an alternating optimization framework that partitions the problem into several blocks, which are optimized in an iterative manner. More specifically, the transmit precoding vectors are optimized by reformulating the problem as a convex semidefinite programming problem through successive-convex approximation (SCA), whereas the inherently convex power-splitting problem is solved using the MOSEK-enabled CVX toolbox. Subsequently, to optimize the scattering matrix of the BD-RIS, we first employ SCA to reformulate the problem into a convex form, and then design a manifold optimization strategy based on the conjugate-gradient method. Finally, numerical simulations are conducted to evaluate the performance of the proposed scheme, revealing significant performance improvements over existing benchmarks and demonstrating rapid convergence within a reasonable number of iterations.