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

2025-12-23

AI Summary

Existing works predominantly focus on downlink and single-cell Rate-Splitting Multiple Access (RSMA); large-scale uplink RSMA remains unmodeled and unanalyzed. Method: This paper establishes the first stochastic geometric analytical framework for large-scale uplink RSMA, incorporating finite modulation-and-coding-scheme (MCS)-based rate adaptation and jointly capturing spatial interference coupling and discrete rate selection. Contribution/Results: We derive, for the first time, closed-form expressions for the conditional reception rate (CRR), its spatial average, and higher-order statistics; further, we employ the meta-distribution to characterize user-level rate performance, revealing how rate discretization intrinsically affects interference dynamics and fairness. The framework subsumes NOMA and OMA as special cases. Both theoretical analysis and simulations confirm that RSMA significantly enhances average spectral efficiency and edge-user fairness in dense networks.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Analyzes uplink RSMA performance in large-scale wireless networks
• Develops stochastic geometry framework with discrete rate adaptation
• Quantifies interference dynamics and fairness in dense RSMA deployments

Innovation

• Uses stochastic geometry to model uplink RSMA in large-scale networks
• Integrates finite MCS-based rate adaptation for realistic interference analysis
• Derives tractable expressions for conditional received rate and meta distribution