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arXiv · 2412.19475

Exploiting Dynamic Sparsity for Near-Field Spatial Non-Stationary XL-MIMO Channel Tracking

Wenkang Xu·An Liu·Min-jian Zhao·Giuseppe Caire·Yik-Chung Wu
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Abstract

This work considers a spatial non-stationary channel tracking problem in broadband extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. In the case of spatial non-stationary, each scatterer has a certain visibility region (VR) over antennas and power change may occur among visible antennas. Concentrating on the temporal correlation of XL-MIMO channels, we design a three-layer Markov prior model and hierarchical two-dimensional (2D) Markov model to exploit the dynamic sparsity of sparse channel vectors and VRs, respectively. Then, we formulate the channel tracking problem as a bilinear measurement process, and a novel dynamic alternating maximum a posteriori (DA-MAP) framework is developed to solve the problem. The DA-MAP contains four basic modules: channel estimation module, VR detection module, grid update module, and temporal correlated module. Specifically, the first module is an inverse-free variational Bayesian inference (IF-VBI) estimator that avoids computational intensive matrix inverse each iteration; the second module is a turbo compressive sensing (Turbo-CS) algorithm that only needs small-scale matrix operations in a parallel fashion; the third module refines the polar-delay domain grid; and the fourth module can process the temporal prior information to ensure high-efficiency channel tracking. Simulations show that the proposed method can achieve a significant channel tracking performance while achieving low computational overhead.

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Wenkang Xu, An Liu, Min-jian Zhao, Giuseppe Caire, Yik-Chung Wu. 2024-12-27. Exploiting Dynamic Sparsity for Near-Field Spatial Non-Stationary XL-MIMO Channel Tracking. https://arxiv.org/abs/2412.19475

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