LIDAR Data for Deep Learning-Based mmWave Beam-Selection

TL;DR

This paper demonstrates how LIDAR data can be utilized with deep learning to improve millimeter wave beam selection in vehicle-to-infrastructure communication, reducing configuration overhead.

Contribution

It introduces a novel distributed architecture using LIDAR and deep neural networks for efficient beam selection in mmWave V2I links.

Findings

LIDAR data improves line-of-sight detection accuracy.

Deep learning reduces beam-selection overhead.

Simulation confirms effectiveness in V2I scenarios.

Abstract

Millimeter wave communication systems can leverage information from sensors to reduce the overhead associated with link configuration. LIDAR (light detection and ranging) is one sensor widely used in autonomous driving for high resolution mapping and positioning. This paper shows how LIDAR data can be used for line-of-sight detection and to reduce the overhead in millimeter wave beam-selection. In the proposed distributed architecture, the base station broadcasts its position. The connected vehicle leverages its LIDAR data to suggest a set of beams selected via a deep convolutional neural network. Co-simulation of communications and LIDAR in a vehicle-to-infrastructure (V2I) scenario confirm that LIDAR can help configuring mmWave V2I links.