Adaptive Subarray Segmentation: A New Paradigm of Spatial Non-Stationary Near-Field Channel Estimation for XL-MIMO Systems

To tackle the complexities of spatial non-stationary (SnS) effects and spherical wave propagation in near-field channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems, this paper introduces an innovative SnS near-field CE framework grounded in adaptive subarray partitioning. Conventional methods relying on equal subarray partitioning often lead to suboptimal divisions, undermining CE precision. To overcome this, we propose an adaptive subarray segmentation approach. First, we develop a spherical-wave channel model customized for line-of-sight (LoS) XL-MIMO systems to capture SnS traits. Next, we define and evaluate the adverse effects of over-segmentation and under-segmentation on CE efficacy. To counter these issues, we introduce a novel dynamic hybrid beamforming-assisted power-based subarray segmentation paradigm (DHBF-PSSP), which merges cost-effective power measurements with a DHBF structure, enabling joint subarray partitioning and decoupling. A robust partitioning algorithm, termed power-adaptive subarray segmentation (PASS), exploits statistical features of power profiles, while the DHBF utilizes subarray segmentation-based group time block code (SS-GTBC) to enable efficient subarray decoupling with limited radio frequency (RF) chain resources. Additionally, by utilizing angular-domain block sparsity and inter-subcarrier structured sparsity, we propose a subarray segmentation-based assorted block sparse Bayesian learning algorithm under the multiple measurement vectors framework (SS-ABSBL-MMV), employing discrete Fourier transform (DFT) codebooks to lower complexity. Extensive simulation results validate the exceptional performance of the proposed framework over its counterparts.