Sparse Near-Field Channel Estimation for XL-MIMO via Adaptive Filtering

TL;DR

This paper introduces a novel adaptive filtering method for sparse near-field channel estimation in XL-MIMO systems, significantly improving accuracy and reducing complexity over existing techniques at sub-THz frequencies.

Contribution

It proposes the PD-ZALMS algorithm, a new adaptive filtering approach tailored for near-field XL-MIMO channel estimation, outperforming existing methods in accuracy and efficiency.

Findings

PD-ZALMS achieves higher estimation accuracy.

It has lower computational complexity.

Outperforms oracle LS estimator at low-to-moderate SNR.

Abstract

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems operating at sub-THz carrier frequencies represent a promising solution to meet the demands of next-generation wireless applications. This work focuses on sparse channel estimation for XL-MIMO systems operating in the near-field (NF) regime. Assuming a practical subarray-based architecture, we develop a NF channel estimation framework based on adaptive filtering, referred to as \textit{polar-domain zero-attracting least mean squares (PD-ZALMS)}. The proposed method achieves significantly superior channel estimation accuracy and lower computational complexity compared with the well-established polar-domain orthogonal matching pursuit. In addition, the proposed PD-ZALMS is shown to outperform the oracle least-squares channel estimator at low-to-moderate signal-to-noise ratio.