Hashing Beam Training for Near-Field Communications

TL;DR

This paper introduces an efficient hashing-based beam training scheme for millimeter-wave near-field communications, significantly reducing training overhead while maintaining high accuracy, applicable to both near-field and far-field scenarios.

Contribution

The paper proposes a novel hashing multi-arm beam training method leveraging polar domain sparsity for near-field mmWave communications, improving efficiency and accuracy over existing techniques.

Findings

Reduces beam training overhead to logarithmic level.

Achieves 96.4% accuracy compared to exhaustive methods.

Applicable to both near-field and far-field scenarios.

Abstract

In this paper, we investigate the millimeter-wave (mmWave) near-field beam training problem to find the correct beam direction. In order to address the high complexity and low identification accuracy of existing beam training techniques, we propose an efficient hashing multi-arm beam (HMB) training scheme for the near-field scenario. Specifically, we first design a set of sparse bases based on the polar domain sparsity of the near-field channel. Then, the random hash functions are chosen to construct the near-field multi-arm beam training codebook. Each multi-arm beam codeword is scanned in a time slot until all the predefined codewords are traversed. Finally, the soft decision and voting methods are applied to distinguish the signal from different base stations and obtain correctly aligned beams. Simulation results show that our proposed near-field HMB training method can reduce the beam training overhead to the logarithmic level, and achieve 96.4% identification accuracy of exhaustive beam training. Moreover, we also verify applicability under the far-field scenario.