Near-field Beam Training with Sparse DFT Codebook

TL;DR

This paper proposes a novel three-phase near-field beam training scheme using a sparse DFT codebook for XL-array systems, significantly reducing training overhead while maintaining high rate performance.

Contribution

It introduces a sparse DFT codebook that exploits angular periodicity and develops a three-phase beam training scheme for efficient near-field beam search in XL-array systems.

Findings

Achieves 98.67% reduction in beam training overhead compared to exhaustive search.

Maintains high rate performance in high SNR regimes.

Effectively resolves angular ambiguity with a multi-phase approach.

Abstract

Extremely large-scale array (XL-array) has emerged as one promising technology to improve the spectral efficiency and spatial resolution of future sixth generation (6G) wireless systems.The upsurge in the antenna number antennas renders communication users more likely to be located in the near-field region, which requires a more accurate spherical (instead of planar) wavefront propagation modeling.This inevitably incurs unaffordable beam training overhead when performing a two-dimensional (2D) beam-search in both the angular and range domains.To address this issue, we first introduce a new sparse discrete Fourier transform (DFT) codebook, which exhibits the angular periodicity in the received beam pattern at the user, which motivates us to propose a three-phase beam training scheme.Specifically, in the first phase, we utilize the sparse DFT codebook for beam sweeping in an angular subspace and estimate candidate user angles according to the received beam pattern.Then, a central sub-array is activated to scan specific candidate angles for resolving the issue of angular ambiguity and identity the user angle.In the third phase, the polar-domain codebook is applied in the estimated angle to search the best effective range of the user.Finally, numerical results show that the proposed beam training scheme enabled by sparse DFT codebook achieves 98.67% reduction as compared with the exhaustive-search scheme, yet without compromising rate performance in the high signal-to-ratio (SNR) regime.