Revisiting XL-MIMO Channel Estimation: When Dual-Wideband Effects Meet Near Field

The deployment of extremely large antenna arrays (ELAAs) and operation at higher frequency bands in wideband extremely large-scale multiple-input-multiple-output (XL-MIMO) systems introduce significant near-field effects, such as spherical wavefront propagation and spatially non-stationary (SnS) properties. Combined with dual-wideband impacts, these effects fundamentally reshape the sparsity patterns of wideband XL-MIMO channels in the angular-delay domain, making existing sparsity-based channel estimation methods inadequate. To address these challenges, this paper revisits the channel estimation problem for wideband XL-MIMO systems, considering dual-wideband effects, spherical wavefront, and SnS properties. By leveraging the spatial-chirp property of near-field array responses, we quantitatively characterize the sparsity patterns of wideband XL-MIMO channels in the angular-delay domain, revealing global block sparsity and local common-delay sparsity. Building on this structured sparsity, we formulate the wideband XL-MIMO channel estimation problem as a multiple measurement vector (MMV)-based Bayesian inference task and propose a novel column-wise hierarchical prior model to effectively capture the sparsity characteristics. To enable efficient channel reconstruction, we develop an MMV-based variational message passing (MMV-VMP) algorithm, tailored to the complex factor graph induced by the hierarchical prior. Simulation results validate the proposed algorithm, demonstrating its convergence and superior performance compared to existing methods, thus establishing its effectiveness in addressing the challenges of wideband XL-MIMO channel estimation under complex near-field conditions.