  Efficient link configuration in millimeter wave (mmWave) communication
systems is a crucial yet challenging task due to the overhead imposed by beam
selection on the network performance. For vehicle-to-infrastructure (V2I)
networks, side information from LIDAR sensors mounted on the vehicles has been
leveraged to reduce the beam search overhead. In this letter, we propose
distributed LIDAR aided beam selection for V2I mmWave communication systems
utilizing federated training. In the proposed scheme, connected vehicles
collaborate to train a shared neural network (NN) on their locally available
LIDAR data during normal operation of the system. We also propose an
alternative reduced-complexity convolutional NN (CNN) architecture and LIDAR
preprocessing, which significantly outperforms previous works in terms of both
the performance and the complexity.
