Spatial-chirp Codebook-based Hierarchical Beam Training for Extremely Large-Scale Massive MIMO

TL;DR

This paper introduces low-overhead hierarchical beam training schemes for near-field XL-MIMO systems, utilizing spatial-chirp codebooks to effectively address challenges in channel estimation and beamforming in large-scale mmWave and THz communications.

Contribution

It proposes a novel spatial-chirp codebook and hierarchical training method tailored for near-field XL-MIMO, reducing overhead and improving beamforming accuracy.

Findings

Enhanced beamforming performance demonstrated in simulations

Reduced training overhead compared to existing methods

Effective near-field channel representation achieved

Abstract

Extremely large-scale multiple-input multiple-output (XL-MIMO) promises to provide ultrahigh data rates in millimeter-wave (mmWave) and Terahertz (THz) spectrum. However, the spherical-wavefront wireless transmission caused by large aperture array presents huge challenges for channel state information (CSI) acquisition and beamforming. 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 two low-overhead hierarchical beam training schemes for near-field XL-MIMO system. Firstly, we project near-field channel into spatial-angular domain and slope-intercept domain to capture detailed representations. Then we point out three critical criteria for XL-MIMO hierarchical beam training. Secondly, a novel spatial-chirp beam-aided codebook and corresponding hierarchical update policy are proposed. Thirdly, given the imperfect coverage and overlapping of spatial-chirp beams, we further design an enhanced hierarchical training codebook via manifold optimization and alternative minimization. Theoretical analyses and numerical simulations are also displayed to verify the superior performances on beamforming and training overhead.