Towards Real-World 6G Drone Communication: Position and Camera Aided Beam Prediction

TL;DR

This paper introduces a machine learning framework that uses sensory data to improve beam prediction in drone communication, significantly reducing training overhead and enabling efficient 6G drone connectivity.

Contribution

It presents a novel sensing-aided beam prediction method leveraging visual and positional data, validated on real-world drone communication datasets.

Findings

Achieves 86.32% top-1 beam prediction accuracy

Near 100% top-3 and top-5 accuracy

Reduces beam training overhead significantly

Abstract

Millimeter-wave (mmWave) and terahertz (THz) communication systems typically deploy large antenna arrays to guarantee sufficient receive signal power. The beam training overhead associated with these arrays, however, make it hard for these systems to support highly-mobile applications such as drone communication. To overcome this challenge, this paper proposes a machine learning-based approach that leverages additional sensory data, such as visual and positional data, for fast and accurate mmWave/THz beam prediction. The developed framework is evaluated on a real-world multi-modal mmWave drone communication dataset comprising of co-existing camera, practical GPS, and mmWave beam training data. The proposed sensing-aided solution achieves a top-1 beam prediction accuracy of 86.32% and close to 100% top-3 and top-5 accuracies, while considerably reducing the beam training overhead. This highlights a promising solution for enabling highly mobile 6G drone communications.