LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum Training

TL;DR

This paper introduces a novel neural network architecture utilizing LIDAR data and curriculum training to enhance mmWave beam selection in vehicle-to-infrastructure communication, significantly reducing search overhead and improving throughput.

Contribution

The paper presents a lightweight neural network with a new loss function, curriculum training, and non-local attention, outperforming previous methods in mmWave beam selection using LIDAR data.

Findings

Achieves 79.9% throughput of exhaustive search without beam search overhead.

Reaches 95% throughput by testing only 6 beams.

Reduces beam search time compared to existing schemes.

Abstract

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.