有源STAR-RIS辅助RSMA物联网系统：性能分析、基于模型与数据驱动的资源分配框架
Active STAR-RIS-Aided RSMA IoT Systems: Performance Analysis, Model-Based and Data-Driven Resource Allocation Frameworks
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中文总结 AI 辅助
本研究针对有源STAR-RIS辅助RSMA物联网系统，推导了中断概率与遍历容量闭式解，并提出基于模型与数据驱动的资源分配框架，以提升可靠性和公平性。
中文摘要 AI 辅助
STAR-RIS已成为一种有前景的技术，能够以低硬件复杂度和能耗实现全空间信号覆盖。与此同时，RSMA提供了一种灵活的干扰管理机制，可提升频谱效率和用户公平性。将STAR-RIS与RSMA相结合，为稳健且节能的通信提供了巨大潜力；然而，由此产生的双衰落级联信道以及近端与远端用户之间的固有失衡，带来了可靠性和公平性方面的挑战。本研究针对有源STAR-RIS辅助的RSMA物联网系统，提出了全面的信息论和优化框架。在存在和不存在直连链路两种情况下，推导了中断概率（OP）和遍历容量（EC）的闭式表达式。基于这些结果，分析了渐近中断概率、渐近遍历容量、分集阶数和阵列增益，以表征系统行为。此外，研究了基于吞吐量和基于频谱的能效指标，以量化传输性能与功耗之间的权衡。为了增强用户公平性，开发了一个面向公平性的资源分配（RA）框架，利用逐次凸逼近和块坐标下降技术联合优化功率分配和速率分裂系数。为了进一步提高可扩展性和实时适用性，引入了一个基于数据驱动的RA框架，该框架基于DMNN、CMNN和MXGB。该框架以显著降低的计算复杂度逼近基于优化的解决方案。数值结果表明，所提出的MXGB模型实现了最短的执行时间，同时保持了与基于真值的基准相当的性能。广泛的蒙特卡洛模拟验证了分析结果，并证明了所提出框架的有效性。
英文摘要
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.
发表机构
- Van Lang University（万郎大学）
- Ho Chi Minh City University of Transport（胡志明市交通大學）
机构由 AI 辅助整理，请以论文原文为准。
