Large-scale Modeling of NGMA Networks

(2024 - Present) Unified analytical framework for uplink RSMA, NOMA, and OMA under spatial randomness and discrete rate adaptation.

1. Research Background & Motivation

The Shift Toward Next-Generation Multiple Access (NGMA)

To meet the stringent requirements of 6G—such as massive connectivity and ultra-reliability—Rate-Splitting Multiple Access (RSMA) has emerged as a powerful paradigm. By splitting messages and employing Successive Interference Cancellation (SIC), RSMA allows receivers to partially decode interference and partially treat it as noise, providing a flexible balance between interference cancellation and resource allocation.

While downlink RSMA is well-studied, the uplink poses distinct modeling challenges:

  • Decentralized Management: Independent transmissions from spatially distributed users lead to complex inter-cell interference coupling.
  • Channel-Dependent Decoding: In the uplink, decoding orders are determined by relative channel conditions rather than fixed centralized precoding.
  • Spatial Randomness: In dense 6G deployments, the inter-cell interference and spatial coupling become highly intensified, requiring tools beyond deterministic single-cell analysis.

2. Methodology: Stochastic Geometry & Meta Distribution

Our research bridges the gap between theoretical tractability and practical realism by introducing a unified analytical framework based on Stochastic Geometry (SG).

The proposed analytical framework: From large-scale random topology to rate meta-distribution analysis.

Bridging Discrete vs. Continuous Rate Models

Most existing SG-based analyses assume continuous rates based on Shannon’s capacity formula. In contrast, our framework integrates finite Modulation and Coding Scheme (MCS)-based rate adaptation. This approach quantifies the performance based on a finite set of Signal-to-Interference-plus-Noise Ratio (SINR) thresholds, which closely represents practical transmission behavior.

Key Theoretical Contributions

  • Meta Distribution Analysis: Beyond spatially-averaged metrics, we derive the meta distribution of the Conditional Received Rate (CRR). This allows us to characterize not just the mean rate, but also the user-specific reliability and fairness (fine-grained statistics) across the entire network.
  • Unified Analytical Foundation: We provide a generalized model that encompasses OMA, NOMA, and RSMA as special cases. This enables a systematic benchmarking of different access paradigms under identical spatial configurations.

3. Core Insights & Results

The proposed framework provides several new insights into the performance of dense RSMA-enabled networks:

  1. Interference Dynamics: We show how discrete rate adaptation reshapes the interference landscape, revealing that continuous-rate models often overestimate practical performance.
  2. Tractable CRR Expressions: We derived compact, closed-form expressions for the CRR and its higher-order statistics (moments), facilitating rapid system-level evaluation without exhaustive Monte Carlo simulations.
  3. Generalization Gains: Our results demonstrate that RSMA’s ability to manage interference flexibly leads to significant gains in spectral efficiency and fairness compared to traditional NOMA and OMA in large-scale uplink deployments.

Technical Highlights

  • Mathematical Tools: Stochastic Geometry (Point Process Theory), Meta Distribution, Laplace Transforms, and Beta Approximation.
  • Key Metrics: Conditional Received Rate (CRR), Spatially-averaged Spectral Efficiency (SE), and Rate Meta Distribution.
  • Simulation Environment: Unified MATLAB/Python frameworks for cross-paradigm (RSMA/NOMA/OMA) benchmarking.
  • X. Guo, L. You$^\dagger$, Q. Liu$^\dagger$, X. G. Xia, X. Gao, “Uplink RSMA Performance Analysis with Rate Adaptation: A Stochastic Geometry Approach”, Under Review (Journal), 2025.
  • X. Guo, Q. Liu, S. Wang, L. You, X. Gao, “Stochastic Geometry-Based MCS Adaption Analysis in NGMA Networks”, IEEE WCNC, 2025.

($^\dagger$ Corresponding Author)