Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction

TL;DR

This paper introduces a machine learning approach using visual data from drone-mounted cameras to predict wireless beam directions accurately, reducing training overhead for high-mobility mmWave/THz drone communications.

Contribution

It presents a novel vision-aided machine learning method for fast beam prediction in drone communication, supported by a synthetic dataset combining wireless and visual data.

Findings

Achieves approximately 91% top-1 beam prediction accuracy.

Attains nearly 100% top-3 accuracy.

Demonstrates effectiveness for highly mobile drone communication.

Abstract

Millimeter wave (mmWave) and terahertz (THz) drones have the potential to enable several futuristic applications such as coverage extension, enhanced security monitoring, and disaster management. However, these drones need to deploy large antenna arrays and use narrow directive beams to maintain a sufficient link budget. The large beam training overhead associated with these arrays makes adjusting these narrow beams challenging for highly-mobile drones. To address these challenges, this paper proposes a vision-aided machine learning-based approach that leverages visual data collected from cameras installed on the drones to enable fast and accurate beam prediction. Further, to facilitate the evaluation of the proposed solution, we build a synthetic drone communication dataset consisting of co-existing wireless and visual data. The proposed vision-aided solution achieves a top-$1$ beam prediction accuracy of $\approx 91\%$ and close to $100\%$ top-$3$ accuracy. These results highlight the efficacy of the proposed solution towards enabling highly mobile mmWave/THz drone communication.