On the Performance of RSMA-IRS-Aided Cell-Free Massive MIMO Systems Under Finite Blocklength

This paper investigates the integration of rate-splitting multiple access (RSMA) into intelligent reflecting surface (IRS)-aided cell-free massive multiple-input multiple-output (MIMO) systems operating under finite blocklength constraints. We derive tight closed-form expressions for the achievable rates of both common and private streams, incorporating practical system impairments including imperfect channel state information and finite blocklength effects. These analytical expressions serve as fundamental tools for evaluating system performance under diverse configurations. Furthermore, we develop a joint optimization framework to optimize the IRS phase shifts and power control coefficients with the objective of maximizing the common rate. To address the inherent non-convexity of this optimization problem, we propose a two-stage solution methodology: the original problem is decomposed into two sub-problems, where the IRS phase shifts are optimized using a particle swarm optimization-based algorithm, and the common stream power allocation is determined through a geometric programming-based approach. Extensive numerical evaluations demonstrate that the proposed algorithms achieve substantial rate improvements compared to conventional benchmarks, establishing RSMA as a promising technique for enhancing the key performance of IRS-aided cell-free massive MIMO systems.