@inproceedings{1fb1a6c5916e4deca215219dca47d2e8,
title = "Enhancing V2X Communication: Machine Learning Assisted Dynamic mmWave Beam Search",
abstract = "This paper addresses the challenges of dynamic beam search in millimeter wave (mmWave) communications for vehicle-to-everything (V2X) applications. With the rapid mobility of connected autonomous vehicles (CAVs) and dense urban environments, maintaining high-quality mmWave con-nections is critical for the reliability and efficiency of V2X communications. We propose a novel machine learning-assisted framework for dynamic mmWave beam search, which signif-icantly enhances the adaptability and performance of V2X communication systems. Our approach leverages real-time environmental data and CAV dynamics to predict optimal beam directions, improving connection stability. Simulation results demonstrate the effectiveness of the proposed method in a real-world road scenario, offering a partial improvement over conventional beam search techniques.",
keywords = "beam search, beamforming, Connected autonomous vehicle, machine learning, millimeter wave, V2X",
author = "Ryo Iwaki and Jin Nakazato and Kazuki Maruta and Manabu Tsukada and Hideya Ochiai and Hiroshi Esaki",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 15th International Conference on Ubiquitous and Future Networks, ICUFN 2024 ; Conference date: 02-07-2024 Through 05-07-2024",
year = "2024",
doi = "10.1109/ICUFN61752.2024.10625435",
language = "English",
series = "International Conference on Ubiquitous and Future Networks, ICUFN",
publisher = "IEEE Computer Society",
pages = "404--409",
booktitle = "ICUFN 2024 - 15th International Conference on Ubiquitous and Future Networks",
}