Towards Real-World 6G Drone Communication: Position and Camera Aided Beam Prediction
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.
Keywords
Cite
@article{arxiv.2205.12187,
title = {Towards Real-World 6G Drone Communication: Position and Camera Aided Beam Prediction},
author = {Gouranga Charan and Andrew Hredzak and Christian Stoddard and Benjamin Berrey and Madhav Seth and Hector Nunez and Ahmed Alkhateeb},
journal= {arXiv preprint arXiv:2205.12187},
year = {2022}
}
Comments
Submitted to IEEE. Datasets and code files are available on the DeepSense website: https://deepsense6g.net/