Active STAR-RIS-Aided RSMA IoT Systems: Performance Analysis, Model-Based and Data-Driven Resource Allocation Frameworks

Abstract

STAR-RISs have emerged as a promising technology for achieving full-space signal coverage with low hardware complexity and energy consumption. In parallel, RSMA offers a flexible interference management mechanism that enhances spectral efficiency and user fairness. Integrating STAR-RIS with RSMA provides strong potential for robust and energy-efficient communications; however, the resulting double-fading cascaded channels and the inherent imbalance between near and far users pose reliability and fairness challenges. This study presents comprehensive information-theoretic and optimization frameworks for active STAR-RIS-assisted RSMA Internet-of-Things systems. Closed-form expressions for the OP and EC are derived under both the presence and absence of direct links. Based on these results, asymptotic OP and EC, diversity order, and array gain are analyzed to characterize system behavior. In addition, throughput-based and spectral-based energy efficiency metrics are investigated to quantify the tradeoffs between transmission performance and power consumption. To enhance user fairness, a fairness-oriented RA framework is developed to jointly optimize power allocation and rate-splitting coefficients using successive convex approximation and block coordinate descent techniques. To further improve scalability and real-time applicability, we introduce a data-driven RA framework based on DMNN, CMNN, and MXGB. This framework approximates the optimization-based solutions with substantially reduced computational complexity. Numerical results show that the proposed MXGB model achieves the shortest execution time while maintaining performance comparable to ground truth-based benchmarks. Extensive Monte Carlo simulations validate the analytical results and demonstrate the effectiveness of the proposed frameworks.