Deep Learning for Hierarchical Beam Alignment in mmWave Communication Systems

TL;DR

This paper introduces a deep learning-based hierarchical beam alignment method for mmWave systems that improves accuracy and reduces overhead by learning two-tier probing codebooks and predicting optimal beams through a coarse-to-fine approach.

Contribution

It proposes a novel deep neural network architecture that learns hierarchical probing codebooks for efficient beam alignment in mmWave communications.

Findings

Outperforms state-of-the-art methods in accuracy

Reduces beam sweeping overhead

Effective in realistic ray-tracing environments

Abstract

Fast and precise beam alignment is crucial to support high-quality data transmission in millimeter wave (mmWave) communication systems. In this work, we propose a novel deep learning based hierarchical beam alignment method that learns two tiers of probing codebooks (PCs) and uses their measurements to predict the optimal beam in a coarse-to-fine searching manner. Specifically, the proposed method first performs coarse channel measurement using the tier-1 PC, then selects a tier-2 PC for fine channel measurement, and finally predicts the optimal beam based on both coarse and fine measurements. The proposed deep neural network (DNN) architecture is trained in two steps. First, the tier-1 PC and the tier-2 PC selector are trained jointly. After that, all the tier-2 PCs together with the optimal beam predictors are trained jointly. The learned hierarchical PCs can capture the features of propagation environment. Numerical results based on realistic ray-tracing datasets demonstrate that the proposed method is superior to the state-of-art beam alignment methods in both alignment accuracy and sweeping overhead.