Learning Site-Specific Probing Beams for Fast mmWave Beam Alignment

TL;DR

This paper introduces a neural network-based method for fast mmWave beam alignment that learns site-specific probing beams, significantly reducing complexity and latency while maintaining high accuracy in realistic environments.

Contribution

It presents a novel end-to-end neural network approach to learn site-specific probing codebooks for efficient beam alignment in mmWave systems.

Findings

Achieves high beam alignment accuracy and SNR.

Reduces beam sweeping complexity and latency by about three times.

Demonstrates effectiveness using realistic ray-tracing datasets.

Abstract

Beam alignment - the process of finding an optimal directional beam pair - is a challenging procedure crucial to millimeter wave (mmWave) communication systems. We propose a novel beam alignment method that learns a site-specific probing codebook and uses the probing codebook measurements to predict the optimal narrow beam. An end-to-end neural network (NN) architecture is designed to jointly learn the probing codebook and the beam predictor. The learned codebook consists of site-specific probing beams that can capture particular characteristics of the propagation environment. The proposed method relies on beam sweeping of the learned probing codebook, does not require additional context information, and is compatible with the beam sweeping-based beam alignment framework in 5G. Using realistic ray-tracing datasets, we demonstrate that the proposed method can achieve high beam alignment accuracy and signal-to-noise ratio (SNR) while significantly - by roughly a factor of 3 in our setting - reducing the beam sweeping complexity and latency.