DL-Based Beam Management for mmWave Vehicular Networks Exploring Temporal Correlation

TL;DR

This paper introduces a deep learning framework for beam management in mmWave vehicular networks, improving robustness and reducing overhead in challenging non-LOS scenarios through recurrent neural networks and position-aware strategies.

Contribution

It presents a novel deep learning-based beam tracking method that supports classification and regression, explicitly addressing non-LOS conditions in vehicular environments.

Findings

Maintains high top-K accuracy in non-LOS scenarios.

Reduces beam measurement overhead by up to 50%.

Supports both classification and regression models.

Abstract

Millimeter wave communications are essential for modern wireless networks. It supports high data rates but suffers from severe path loss, which requires precise beam alignment to maintain reliable links. This beam management is particularly challenging in highly dynamic scenarios such as vehicle-to-infrastructure, and several methods have been presented. In this work, we propose a deep learning-based beam tracking framework that combines a position-aware beam pre-selection strategy with sequential prediction using recurrent neural networks. The proposed architecture can support deep learning models trained for both classification and regression. In contrast to many existing studies that evaluate beam tracking under predominantly line-of-sight (LOS) conditions, our work explicitly includes highly challenging non-LOS scenarios - with up to 50% non-LOS incidence in certain datasets - to rigorously assess model robustness. Experimental results demonstrate that our approach maintains high top-K accuracy, even under adverse conditions, while reducing the beam measurement overhead by up to 50%.