Low-Complexity Channel Estimation Framework for Non-Square UPA-Assisted XL-MIMO Systems
Abstract
Low-complexity channel state information acquisition is crucial for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. However, practical deployments of non-square uniform planar arrays (UPAs) in hybrid-field environments face prohibitive computational complexity and degraded estimation accuracy due to limited elevation angle-of-arrival (AoA) resolution and deteriorated channel sparsity. To tackle these challenges, we propose a low-complexity channel estimation framework. First, an antenna-domain extrapolation scheme synthesizes a virtually enlarged vertical aperture via the spatial correlation among adjacent elements, breaking the elevation resolution limit. The framework then disentangles the parameter coupling by transforming the two-dimensional joint search into two sequential one-dimensional searches. Specifically, elevation AoAs are extracted via an extrapolation-enhanced discrete Fourier transform-Newtonized orthogonal matching pursuit (NOMP) algorithm along the virtually enlarged vertical uniform linear array (ULA), while azimuth AoAs, ranges, and gains are acquired utilizing a discrete fractional Fourier transform-NOMP algorithm along a horizontal ULA. A subspace fitting-driven path matching algorithm pairs these decoupled parameters. To overcome the accuracy bottleneck of the antenna-domain scheme, a correlation-domain extrapolation scheme is further developed by exploiting the structural properties of the spatial correlation matrix to decouple the near-field quadratic and azimuth phase components, yielding a noise-suppressed virtual array. Numerical results validate the effectiveness of the proposed framework.
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BibTeX
@misc{liu2026lowcomplexity,
title = {{Low-Complexity Channel Estimation Framework for Non-Square UPA-Assisted XL-MIMO Systems}},
author = {Yilong Liu and Xi Yang and Binggui Zhou and Yu Han and Ting Liu and Shaodan Ma},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2607.18744},
eprint = {2607.18744},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.18744},
}
RIS
TY - UNPB
TI - Low-Complexity Channel Estimation Framework for Non-Square UPA-Assisted XL-MIMO Systems
AU - Yilong Liu
AU - Xi Yang
AU - Binggui Zhou
AU - Yu Han
AU - Ting Liu
AU - Shaodan Ma
PY - 2026
DA - 2026/10/14
PB - arXiv
DO - 10.48550/arxiv.2607.18744
UR - https://arxiv.org/abs/2607.18744
AB - Low-complexity channel state information acquisition is crucial for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. However, practical deployments of non-square uniform planar arrays (UPAs) in hybrid-field environments face prohibitive computational complexity and degraded estimation accuracy due to limited elevation angle-of-arrival (AoA) resolution and deteriorated channel sparsity. To tackle these challenges, we propose a low-complexity channel estimation framework. First, an antenna-domain extrapolation scheme synthesizes a virtually enlarged vertical aperture via the spatial correlation among adjacent elements, breaking the elevation resolution limit. The framework then disentangles the parameter coupling by transforming the two-dimensional joint search into two sequential one-dimensional searches. Specifically, elevation AoAs are extracted via an extrapolation-enhanced discrete Fourier transform-Newtonized orthogonal matching pursuit (NOMP) algorithm along the virtually enlarged vertical uniform linear array (ULA), while azimuth AoAs, ranges, and gains are acquired utilizing a discrete fractional Fourier transform-NOMP algorithm along a horizontal ULA. A subspace fitting-driven path matching algorithm pairs these decoupled parameters. To overcome the accuracy bottleneck of the antenna-domain scheme, a correlation-domain extrapolation scheme is further developed by exploiting the structural properties of the spatial correlation matrix to decouple the near-field quadratic and azimuth phase components, yielding a noise-suppressed virtual array. Numerical results validate the effectiveness of the proposed framework.
ER -