2024/12/27 by Xu, Wenkang, Minjian Zhao, Liu, An +5 · 1 citation
Engineering · #Advanced MIMO Systems Optimization #Energy Harvesting in Wireless Networks #FOS: Electrical engineering #Full-Duplex Wireless Communications #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2412.19475
openalex publication_date 2024/12/27 · openalex created_date 2024/12/31 · openalex updated_date 2026/07/28
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.