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

TL;DR

This paper analyzes the performance of rate-splitting in cell-free massive MIMO systems with imperfect CSI and proposes AI-based optimization techniques to enhance spectral efficiency and interference mitigation.

Contribution

It introduces a comprehensive analysis of RS-based CF massive MIMO with imperfect CSI and develops AI-driven algorithms for optimizing system performance.

Findings

Closed-form sum spectral efficiency expression derived

Proposed low-complexity heuristic algorithms for power control

AI-based diffusion model effectively optimizes nonconvex problems

Abstract

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.