GNN-Based RSMA for Energy Efficiency in Hardware-Impaired Cell-Free URLLC Systems

This work explores the maximization of energy efficiency (EE) in cell-free ultra-reliable low-latency communication (URLLC) systems, specifically addressing the challenges presented by hardware impairments (HWIs). We introduce ratesplitting multiple access (RSMA) as an effective technique to enhance EE by managing the distortions caused by HWIs during downlink transmission. The optimization problem is formulated to maximize EE by optimizing precoding vectors for both common and private streams, while adhering to rate, power, and URLLC constraints. To address the non-convex and dynamic nature of this optimization problem, we propose a graph neural network (GNN) model that facilitates scalable and data-efficient solutions. Simulation results demonstrate that the proposed RSMA-GNN method consistently outperforms baseline approaches, including space-division multiple access (SDMA) and successive convex approximation (SCA) methods, particularly in scenarios characterized by severe HWIs.