MmWave Vehicular Beam Training with Situational Awareness by Machine Learning

Abstract

Configuring beams in millimeter-wave (mmWave) vehicular communication is a challenging task. Large antenna arrays and narrow beams are deployed at transceivers to exploit beamforming gain, which leads to significant system overhead if an exhaustive beam search is adopted. In this paper, we propose to learn the optimal beam pair index by exploiting the locations and sizes of the receiver and its neighboring vehicles (parts of the situational awareness for automated driving), leveraging machine learning tools with the past beam training records. MmWave beam selection is formulated as a classification problem based on situational awareness. We provide a comprehensive comparison of different classification models and various levels of situational awareness. Practical issues are considered in realistic implementations, including GPS inaccuracies, out-dated locations due to fixed location reporting frequencies and missing features with limited connected vehicles penetration rate. The result shows that we can achieve up to 86% of alignment probability with ideal assumptions.