Chirp-based Hierarchical Beam Training for Extremely Large-Scale Massive MIMO

TL;DR

This paper introduces a low-overhead hierarchical beam training scheme for near-field XL-MIMO systems in the Terahertz spectrum, leveraging spatial-angular and slope-intercept domain projections to improve beamforming accuracy and reduce training overhead.

Contribution

It proposes a novel spatial-chirp beam-aided codebook and hierarchical update policy tailored for near-field XL-MIMO, addressing the challenges of spherical-wavefront transmission and dual-parameter considerations.

Findings

Enhanced beamforming accuracy in near-field XL-MIMO.

Reduced training overhead compared to existing methods.

Theoretical and simulation results confirm performance improvements.

Abstract

XL-MIMO promises to provide ultrahigh data rates in Terahertz (THz) spectrum. However, the spherical-wavefront wireless transmission caused by large aperture array presents huge challenges for channel state information (CSI) acquisition. Two independent parameters (physical angles and transmission distance) should be simultaneously considered in XL-MIMO beamforming, which brings severe overhead consumption and beamforming degradation. To address this problem, we exploit the near-field channel characteristic and propose one low-overhead hierarchical beam training scheme for near-field XL-MIMO system. Firstly, we project near-field channel into spatial-angular domain and slope-intercept domain to capture detailed representations. Secondly, a novel spatial-chirp beam-aided codebook and corresponding hierarchical update policy are proposed. Theoretical analyses and numerical simulations are also displayed to verify the superior performances on beamforming and training overhead.