Non-Stationary Near-Field Channel Estimation for XL-MIMO Systems With Hybrid Combining

In this letter, we study the spatial non-stationary near-field channel estimation (CE) of the extremely large-scale multiple-input multiple-output system with hybrid combining. In the case of known partition of the antenna sub-arrays, the CE is first formulated as a structured compressed sensing problem, and then the two-level polar-domain orthogonal matching pursuit (OMP) algorithm is presented to determine the channel quantization index and the visibility region (VR). In the case of unknown partition, a list-type CE method is proposed. Specifically, the initial estimation of the VR is first acquired by employing the variational block sparse recovery algorithm with unknown partition. Then, the candidate set of the VR is obtained based on proper majority-logic decision and enumeration, and for each candidate, the channel quantization index is estimated by the OMP algorithm. Finally, the effectiveness of the proposed methods are demonstrated by computer simulations.

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Publication Details

Journal
IEEE Wireless Communications Letters
Published
2024-08-12
DOI
https://doi.org/10.1109/lwc.2024.3442769
Citations
10
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
2.09

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article

Non-Stationary Near-Field Channel Estimation for XL-MIMO Systems With Hybrid Combining

Jianping Zheng, Xiuyu Zhang
10 citations
IEEE Wireless Communications Letters
Advanced MIMO Systems Optimization
2.09
article

Non-Stationary Near-Field Channel Estimation for XL-MIMO Systems With Hybrid Combining

Jianping Zheng, Xiuyu Zhang
article en
10 citations

Abstract

In this letter, we study the spatial non-stationary near-field channel estimation (CE) of the extremely large-scale multiple-input multiple-output system with hybrid combining. In the case of known partition of the antenna sub-arrays, the CE is first formulated as a structured compressed sensing problem, and then the two-level polar-domain orthogonal matching pursuit (OMP) algorithm is presented to determine the channel quantization index and the visibility region (VR). In the case of unknown partition, a list-type CE method is proposed. Specifically, the initial estimation of the VR is first acquired by employing the variational block sparse recovery algorithm with unknown partition. Then, the candidate set of the VR is obtained based on proper majority-logic decision and enumeration, and for each candidate, the channel quantization index is estimated by the OMP algorithm. Finally, the effectiveness of the proposed methods are demonstrated by computer simulations.

IEEE Wireless Communications LettersVol. 13(10)
Xidian University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 13%
Advanced MIMO Systems Optimization
2.09
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