# Rate-Splitting for Cell-Free Massive MIMO: Performance Analysis and Generative AI Approach

arXiv: https://arxiv.org/abs/2409.14702 · HTML: https://mlsanity.com/paper/2409.14702/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2024.

## Built on by (most cited first, 2 of 2)

- [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025)
- [Generative AI-Driven Phase Control for RIS-Aided Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2602.11226/) (2026)

## Abstract (arXiv)

Cell-free (CF) massive multiple-input multipleoutput (MIMO) provides a ubiquitous coverage to user equipments (UEs) but it is also susceptible to interference. Ratesplitting (RS) effectively extracts data by decoding interference, yet its effectiveness is limited by the weakest UE. In this paper, we investigate an RS-based CF massive MIMO system, which combines strengths and mitigates weaknesses of both approaches. Considering imperfect channel state information (CSI) resulting from both pilot contamination and noise, we derive a closed-form expression for the sum spectral efficiency (SE) of the RS-based CF massive MIMO system under a spatially correlated Rician channel. Moreover, we propose low-complexity heuristic algorithms based on statistical CSI for power-splitting of common messages and power-control of private messages, and genetic algorithm is adopted as a solution for upper bound performance. Furthermore, we formulate a joint optimization problem, aiming to maximize the sum SE of the RS-based CF massive MIMO system by optimizing the power-splitting factor and power-control coefficient. Importantly, we improve a generative AI (GAI) algorithm to address this complex and nonconvexity problem by using a diffusion model to obtain solutions. Simulation results demonstrate its effectiveness and practicality in mitigating interference, especially in dynamic environments.