Neural Enhanced Variational Bayesian Inference on Graphs for Localized Statistical Channel Modeling

This paper proposes an innovative graph neural network (GNN)-based approach to address the challenge of recovering ill-conditioned sparse signals within the task of multi-grid localized statistical channel modeling (LSCM). Our proposed GNN architecture captures the structural sparsity inherent in the channel angular power spectrum (APS) by leveraging reference signal receiving power (RSRP) measured from multiple grids. It can effectively mitigate the severe coherence in the measurement matrix. Furthermore, we present a novel online unsupervised training scheme that enables real-time adaptability for multi-grid LSCM applications. Through extensive simulations, we demonstrate the superior performance of our GNN-based method in the context of multi-grid LSCM, showcasing its advantages over existing sparse recovery techniques.