Hypergraph Neural Network Assisted Robust Beamforming for Cell-Free Massive MIMO

Cell-free massive MIMO (CF mMIMO) systems overcome inter-cell interference, enhancing overall communication rates for next-generation networks. However, the pilot contamination exacerbates channel estimation errors and the complex connectivity makes it difficult to deal with resource allocation optimization problem. In this paper, we investigate the robust beamforming problem under channel uncertainty with the goal of improving the minimum quantile rate. Specifically, we introduce hypergraph neural network (HGNN) into the wireless resource allocation of CF mMIMO ststems for the first time, leveraging hypergraph modeling to capture the many-to-many relationships between Access Points (APs) and User Equipments (UEs). Furthermore, we significantly reduce the search space of the optimization problem by applying optimal interference suppression beamforming theory. In order to soften the sorting process, we adopt the Monte Carlo sampling strategy for data augmentation. Simulation results demonstrate that the proposed algorithm outperforms conventional schemes, achieving 14.1% performance gain and converging more than twice as fast as the state-of-the-art machine learning models.