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

TL;DR

This paper develops a stochastic geometry-based analytical framework for uplink RSMA that incorporates rate adaptation, providing insights into interference management, rate performance, and fairness in large-scale wireless networks.

Contribution

It introduces a unified framework for uplink RSMA analysis that includes finite MCS-based rate adaptation, bridging theoretical models and practical system behavior.

Findings

Framework generalizes NOMA and OMA analyses.

Discrete rate adaptation impacts interference and fairness.

Provides tractable expressions for user-specific and average rates.

Abstract

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 and single-cell designs, the modeling and analysis of uplink RSMA under large-scale deployments remain largely unexplored. On the basis of stochastic geometry (SG), this paper introduces a unified analytical framework that integrates finite modulation and coding scheme (MCS)-based rate adaptation. This framework jointly captures spatial interference coupling and discrete rate behavior to bridge theoretical tractability and practical realism. 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 the mean and user-specific rate performance. Results show that the proposed unified framework not only generalizes existing non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) analyses but also provides new insights into how discrete rate adaptation reshapes interference dynamics and fairness in dense RSMA-enabled networks.