Uplink RSMA Performance Analysis With Rate Adaptation: A Stochastic Geometry Approach

Rate-splitting multiple access (RSMA) has emerged as a promising technique for efficient interference management in next-generation wireless networks. While most existing studies focus on downlink or single-cell designs, the modeling and analysis of uplink RSMA under large-scale deployments remain largely unexplored. Based on stochastic geometry (SG), this paper develops a unified analytical framework that jointly captures spatial interference coupling and discrete modulation and coding scheme (MCS)-based rate adaptation. Within this framework, we derive tractable expressions for the conditional received rate (CRR), its spatial average, and higher-order statistics via the meta distribution, thereby quantifying both average and user-specific rate performance. The proposed framework generalizes the existing analyses of non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA), offering a common analytical ground for fair comparison across access paradigms. Numerical results show that while RSMA and NOMA achieve identical performance under ideal continuous rate adaptation, RSMA provides a significant gain under discrete MCS by exploiting additional degrees of freedom through rate splitting. Moreover, the rate-splitting ratio provides a tunable trade-off between average rate and per-user fairness.