The deployment of extremely large antenna arrays (ELAAs) in extremely large-scale multiple-input multiple-output (XL-MIMO) systems introduces significant near-field effects, such as spherical wavefront propagation and spatially non-stationary (SnS) properties. When combined with the dual-wideband effects inherent to wideband systems, these phenomena fundamentally alter the channel’s sparsity patterns in the angular-delay domain, rendering existing estimation methods insufficient. To address these challenges, this paper reconsiders the channel estimation problem for wideband XL-MIMO systems. Leveraging the spatial-chirp property of array responses, we first quantitatively characterize the angular-delay domain sparsity of wideband XL-MIMO channels, revealing both global block sparsity and local common-delay sparsity. To effectively capture this structured sparsity, we then propose a novel column-wise hierarchical prior model that integrates a precision sharing mechanism and a Markov random field (MRF) structure. Building on this prior model, the channel estimation task is formulated as a multiple measurement vector (MMV)-based Bayesian inference problem. Tailored to the complex factor graph induced by this hierarchical prior, we develop a MMV-based hybrid message passing (MMV-HMP) algorithm. This algorithm performs message updates along the edges of the factor graph, and selectively applies either the variational message passing (VMP) or sum-product (SP) rules, depending on the factor-node structure and message tractability. Simulation results validate the effectiveness of the proposed column-wise hierarchical prior model through ablation studies and demonstrate that the MMV-HMP algorithm, while maintaining moderate computational complexity, consistently outperforms existing baselines which fail to capture the structured sparsity of wideband XL-MIMO channels.

This paper analyzes the spatial multiplexing capability in the line-of-sight (LoS) extremely large-scale multiple-input multiple-output (XL-MIMO) systems, where the impacts of array geometry (such as the shape, size, position, and orientation) on spatial degrees of freedom (DoF) is presented, resulting the validation of promising performance gain of the fluid antenna system (FAS). To this end, we first provide an exact closed-form expression for the local spatial bandwidth at the center of the receive array. Then, we analyze the maximum local spatial bandwidth at different spatial positions. An approximate closed-form expression for the achievable spatial DoF is obtained based on the derived local spatial bandwidth. Simulation results are presented for validation.

Existing channel estimation algorithms for ex-tremely large-scale multiple-input multiple-output (XL-MIMO) systems are predominantly designed for single-user scenarios and often overlook inter-user correlations. To address this limitation, this paper reformulates the joint multiuser channel estimation problem as a multiple-measurement vector (MMV)-based sparse signal recovery task. To solve this, we propose a novel row-wise hierarchical prior model that captures the structured sparsity of the joint multiuser channel in the angular-delay domain. Specifically, shared precision parameters for each row of the angular-delay domain channel are introduced to model common-row sparsity, while a Markov random field (MRF) is employed to encourage cluster sparsity. Building on this structured prior, we develop a computationally effi-cient channel estimation algorithm using variational message passing. Simulation results demonstrate that the proposed method significantly outperforms existing single-user-based approaches.

In this paper, we investigate the channel estimation problem for extremely large-scale multiple-input multiple-output (XL-MIMO) systems with a hybrid analog-digital architecture, implemented within a decentralized baseband processing (DBP) framework with a star topology. Existing centralized and fully decentralized channel estimation methods face limitations due to excessive computational complexity or degraded performance. To overcome these challenges, we propose a novel two-stage channel estimation scheme that integrates local sparse reconstruction with global fusion and refinement. Specifically, in the first stage, by exploiting the sparsity of channels in the angular-delay domain, the local reconstruction task is formulated as a sparse signal recovery problem. To solve it, we develop a graph neural networks-enhanced sparse Bayesian learning (SBL-GNNs) algorithm, which effectively captures dependencies among channel coefficients, significantly improving estimation accuracy. In the second stage, the local estimates from the local processing units (LPUs) are aligned into a global angular domain for fusion at the central processing unit (CPU). Based on the aggregated observations, the channel refinement is modeled as a Bayesian denoising problem. To efficiently solve it, we devise a variational message passing algorithm that incorporates a Markov chain-based hierarchical sparse prior, effectively leveraging both the sparsity and the correlations of the channels in the global angular-delay domain. Simulation results show the effectiveness and superiority of the proposed SBL-GNNs algorithm over existing methods, demonstrating improved estimation performance and reduced computational complexity.

With the combination of extra-large arrays and high frequencies, near-field transmissions have become prevalent, challenging the validity of classical channel representations typically derived under the plane wavefront assumption. In this paper, we investigate the angular-domain representation of line-of-sight (LoS) extra-large MIMO (XL-MIMO) channels, considering the impact of spherical wavefront effects. First, we demonstrate the structured sparsity of LoS XL-MIMO channels in the angular domain. Leveraging this sparsity, we propose an effective spatial bandwidth channel representation method, which characterizes near-field LoS XL-MIMO channels as a superposition of multiple plane wave components, enabling us to capture the spherical wavefront effect in a low-dimensional angular channel. Subsequently, we introduce an angular-domain transceiver architecture based on this low-dimensional channel representation. This architecture could significantly facilitate the implementation of LoS XL-MIMO systems. Finally, simulation results confirm the effectiveness of the effective spatial bandwidth identification method and analyze the impact of various array geometries on the effective spatial bandwidth. Additionally, the availability of the angular-domain processing architecture is validated.

In this work, we investigate the joint visibility region (VR) detection and channel estimation (CE) problem for extremely large-scale multiple-input-multiple-output (XL-MIMO) systems considering both the spherical wavefront effect and spatial non-stationary (SnS) property. Unlike existing SnS CE methods that rely on the statistical characteristics of channels in the spatial or delay domain, we propose an approach that simultaneously exploits the antenna-domain spatial correlation and the wavenumber-domain sparsity of SnS channels. To this end, we introduce a two-stage VR detection and CE scheme. In the first stage, the belief regarding the visibility of antennas is obtained through a VR detection-oriented message passing (VRDO-MP) scheme, which fully exploits the spatial correlation among adjacent antenna elements. In the second stage, leveraging the VR information and wavenumber-domain sparsity, we accurately estimate the SnS channel employing the belief-based orthogonal matching pursuit (BB-OMP) method. Simulations show that the proposed algorithms lead to a significant enhancement in VR detection and CE accuracy as compared to existing methods, especially in low signal-to-noise ratio (SNR) scenarios.

With the combination of extra-large arrays and high frequencies, near-field transmissions have become increasingly prevalent. In this paper, we investigate the angular-domain representation of near-field line-of-sight (LoS) extra-large multiple-input-multiple-output (XL-MIMO) channels. Specifically, we first demonstrate the structured sparsity of the near-field LoS channel in the angular domain. By leveraging this sparsity property, we propose an effective spatial bandwidth channel representation method. This method characterizes near-field LoS XL-MIMO channels as a superposition of multiple plane wave components within the effective spatial band between transceiver arrays. Finally, simulation results validate the equivalence between the proposed representation and the existing antenna domain channel model and demonstrate the effects of array geometries on the effective spatial bandwidth.