Towards Robustness: Machine Learning for MmWave V2X with Situational Awareness

High nobility poses challenges to millimeter wave (mmWave) vehicular beam tracking. In this paper, we propose to recommend a set of candidate beam pairs using machine learning classification and ranking, based on the target vehicle's situational awareness. We observe that the optimal beam directions can be captured by the location of the receiver and the sizes and locations of the neighboring vehicles. This information is a natural part of the situational awareness exploited by connected and automated vehicles. The infrastructure conducts the beam search within the reconunended beam set with the receiver. To further reduce overhead, we leverage a new beam search mechanism, where the search can automatically terminate when the current beam measurement exceeds a specific target power. We set the target power as the predicted average power of the top two beams. The numerical results show that the beam search overhead can be reduced by 70% when seven beams are reconunended, at a comparable beam alignment probability.