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%.
@article{arxiv.2511.02260,
title = {DL-Based Beam Management for mmWave Vehicular Networks Exploring Temporal Correlation},
author = {Ailton Oliveira and Amir Khatibi and Daniel Suzuki and Ilan Correa and José Rezende and Aldebaro Klautau},
journal= {arXiv preprint arXiv:2511.02260},
year = {2025}
}