A High-Accuracy Adaptive Beam Training Algorithm for MmWave Communication

TL;DR

This paper introduces an adaptive beam training algorithm for mmWave communication that allocates resources based on beam gain, significantly improving accuracy over traditional equal allocation methods.

Contribution

The paper proposes a novel adaptive beam training algorithm that dynamically allocates resources according to beam gain, enhancing performance in mmWave systems.

Findings

Adaptive algorithm outperforms traditional methods asymptotically.

Improved beam training accuracy in practical scenarios.

Effective resource allocation based on beam gain.

Abstract

In millimeter wave communications, beam training is an effective way to achieve beam alignment. Traditional beam training method allocates training resources equally to each beam in the pre-designed beam training codebook. The performance of this method is far from satisfactory, because different beams have different beamforming gain, and thus some beams are relatively more difficult to be distinguished from the optimal beam than the others. In this paper, we pro- pose a new beam training algorithm which adaptively allocates training resources to each beam. Specifically, the proposed algorithm allocates more training symbols to the beams with relatively higher beamforming gain, while uses less resources to distinguish the beams with relatively lower beamforming gain. Through theoretical analysis and numerical simulations, we show that in practical situations the proposed adaptive algorithm asymptotically outperforms the traditional method in terms of beam training accuracy. Moreover, simulations also show that this relative performance behavior holds in non-asymptotic regime.