Joint Visibility Region Detection and Channel Estimation for XL-MIMO Systems via Alternating MAP

We investigate a joint visibility region (VR) detection and channel estimation problem in extremely large-scale multiple-input-multiple-output (XL-MIMO) systems, where near-field propagation and spatial non-stationary effects exist. In this case, each scatterer can only see a subset of antennas, i.e...

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Bibliographic Details
Published inIEEE transactions on signal processing Vol. 72; pp. 4827 - 4842
Main Authors Xu, Wenkang, Liu, An, Zhao, Min-jian, Caire, Giuseppe
Format Journal Article
LanguageEnglish
Published New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
2D clustered sparsity
Algorithms
alternating MAP
Antenna arrays
Antennas
Bayesian analysis
Channel estimation
Clustering algorithms
Complexity theory
IF-VBI
Inference algorithms
Inverse matrices
Massive MIMO
Matching pursuit algorithms
Millimeter wave communication
MIMO communication
Modules
Parameter estimation
Signal processing algorithms
Sparsity
Statistical inference
structured EP
Vectors
Visibility
VR detection
XL-MIMO
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Summary:We investigate a joint visibility region (VR) detection and channel estimation problem in extremely large-scale multiple-input-multiple-output (XL-MIMO) systems, where near-field propagation and spatial non-stationary effects exist. In this case, each scatterer can only see a subset of antennas, i.e., it has a certain VR over the antennas. Because of the spatial correlation among adjacent sub-arrays, VR of scatterers exhibits a two-dimensional (2D) clustered sparsity. We design a 2D Markov prior model to capture such a structured sparsity. Based on this, a novel alternating maximum a posteriori (MAP) framework is developed for high-accuracy VR detection and channel estimation. The alternating MAP framework consists of three basic modules: a channel estimation module, a VR detection module, and a grid update module. Specifically, the first module is a low-complexity inverse-free variational Bayesian inference (IF-VBI) algorithm that avoids the matrix inverse via minimizing a relaxed Kullback-Leibler (KL) divergence. The second module is a structured expectation propagation (EP) algorithm which has the ability to deal with complicated prior information. And the third module refines polar-domain grid parameters via gradient ascent. Simulations demonstrate the superiority of the proposed algorithm in both VR detection and channel estimation.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2024.3479319