@inproceedings{21d93bef7cfe4ea7b3d81747053c4f24,
title = "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 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 = "beam selection, camera, computer vision, deep learning, drone, Millimeter wave, position, sensing",
author = "Gouranga Charan and Andrew Hredzak and Christian Stoddard and Benjamin Berrey and Madhav Seth and Hector Nunez and Ahmed Alkhateeb",
note = "Funding Information: VII. ACKNOWLEDGMENT This work is supported in part by the National Science Foundation under Grant No. 2048021. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Global Communications Conference, GLOBECOM 2022 ; Conference date: 04-12-2022 Through 08-12-2022",
year = "2022",
doi = "10.1109/GLOBECOM48099.2022.10000718",
language = "English (US)",
series = "2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2951--2956",
booktitle = "2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings",
}