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

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