  Efficient millimeter wave (mmWave) beam selection in
vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task
due to the narrow mmWave beamwidth and high user mobility. To reduce the search
overhead of iterative beam discovery procedures, contextual information from
light detection and ranging (LIDAR) sensors mounted on vehicles has been
leveraged by data-driven methods to produce useful side information. In this
paper, we propose a lightweight neural network (NN) architecture along with the
corresponding LIDAR preprocessing, which significantly outperforms previous
works. Our solution comprises multiple novelties that improve both the
convergence speed and the final accuracy of the model. In particular, we define
a novel loss function inspired by the knowledge distillation idea, introduce a
curriculum training approach exploiting line-of-sight (LOS)/non-line-of-sight
(NLOS) information, and we propose a non-local attention module to improve the
performance for the more challenging NLOS cases. Simulation results on
benchmark datasets show that, utilizing solely LIDAR data and the receiver
position, our NN-based beam selection scheme can achieve 79.9% throughput of an
exhaustive beam sweeping approach without any beam search overhead and 95% by
searching among as few as 6 beams. In a typical mmWave V2I scenario, our
proposed method considerably reduces the beam search time required to achieve a
desired throughput, in comparison with the inverse fingerprinting and
hierarchical beam selection schemes.
