Low-Complexity Channel Estimation Framework for Non-Square UPA-Assisted XL-MIMO Systems

2026-07-21

AI Summary

This work addresses the challenges of high computational complexity, limited elevation resolution, and degraded sparsity in channel estimation for non-uniform rectangular uniform planar arrays (UPAs) operating in mixed near- and far-field scenarios. To overcome these limitations, a low-complexity channel estimation framework is proposed, featuring a novel dual extrapolation mechanism in both the antenna and correlation domains to extend the virtual aperture, thereby enhancing elevation resolution and suppressing noise. By leveraging the discrete fractional Fourier transform and the Newtonized orthogonal matching pursuit (NOMP) algorithm, the method decouples the two-dimensional joint parameter search into sequential one-dimensional searches. Furthermore, subspace fitting path matching and near-field phase decoupling techniques are integrated to substantially reduce computational burden. Experimental results demonstrate that the proposed approach significantly improves estimation accuracy while effectively mitigating the performance bottlenecks of non-square UPAs in extremely large-scale 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.

Problem

• XL-MIMO
• non-square UPA
• channel estimation
• elevation AoA resolution
• channel sparsity

Innovation

• Channel estimation
• XL-MIMO
• Non-square UPA
• Parameter decoupling
• Extrapolation-based virtual array