Model-Data Hybrid Driven Approach for Channel Estimation in XL-MIMO Systems Under Spatial Non-Stationarity

This paper is concerned with the near-field channel estimation (CE) in extremely large-scale multi-input multi-output (XL-MIMO) systems under spatial non-stationarity (SnS). To this aim, we first analyze the channel characteristics in the systems and design a structured SnS-aware mask matrix, which reveals the relationship between the angular-domain block sparsity of the channel and the SnS structure. Inspired by the relationship, we establish a near-field channel model for the XL-MIMO systems under SnS. Second, we propose a model-data hybrid driven approach, termed SBL4CE-Net, to estimate the near-field channel. SBL4CE-Net unfolds the block sparse Bayesian learning (BSBL) algorithm into a multilayer solution framework and designs customized neural networks (NN) based on parameter features of BSBL to learn its hyperparameters. Simulation results demonstrate that SBL4CE-Net achieves good normalized mean square error (NMSE) performance, demonstrating a balance between interpretability and data-driven adaptability for SnS-affected near-field channels.