AI RESEARCH
GNN-Enabled Robust Hybrid Beamforming with Score-Based CSI Generation and Denoising
arXiv CS.LG
•
ArXi:2511.06663v2 Announce Type: replace-cross Accurate Channel State Information (CSI) is critical for Hybrid Beamforming (HBF) tasks. However, obtaining high-resolution CSI remains challenging in practical wireless communication systems. To address this issue, we propose to utilize Graph Neural Networks (GNNs) and score-based generative models to enable robust HBF under imperfect CSI conditions. Firstly, we develop the Hybrid Message Graph Attention Network (HMGAT) which updates both node and edge features through node-level and edge-level message passing.