Fast Beam Training and Performance Analysis for Extremely Large Aperture Array

TL;DR

This paper introduces a fast, low-overhead hash beam training scheme for extremely large aperture arrays, improving beam alignment efficiency and accuracy through novel codebook designs and performance analysis.

Contribution

The paper proposes two improved hash codebook design methods and a heuristic metric for beam alignment performance evaluation, advancing efficient beam training for ELAA.

Findings

Achieves fast beam alignment with lower overhead

Demonstrates higher accuracy in beam training

Validates theoretical analysis with simulation results

Abstract

Extremely large aperture array (ELAA) can significantly enhance beamforming gain and spectral efficiency. Unfortunately, the use of narrower beams for data transmission results in a substantial increase in the cost of beam training. In this paper, we study a high-efficiency and low-overhead scheme named hash beam training. Specifically, two improved hash codebook design methods, random and fixed, are proposed. Moreover, we analyze beam alignment performance. Since the derived beam alignment success probability is a complex function, we also propose a heuristic metric to evaluate the impact of codebook parameter on performance. Finally, simulation results validate the theoretical analysis, indicating that the proposed beam training scheme can achieve fast beam alignment with lower overhead and higher accuracy.