Sensing-Aided 6G Drone Communications: Real-World Datasets and Demonstration
Gouranga Charan, Ahmed Alkhateeb

TL;DR
This paper presents a machine learning framework that uses multi-modal sensory data to improve beam prediction in 6G drone communications, reducing training overhead and enhancing connectivity in highly mobile scenarios.
Contribution
It introduces a novel multi-modal sensory data-driven approach for beam prediction that outperforms traditional methods in drone communication systems.
Findings
Significantly reduces beam training overhead.
Accurately predicts future beam alignments.
Validated on real-world mmWave drone dataset.
Abstract
In the advent of next-generation wireless communication, millimeter-wave (mmWave) and terahertz (THz) technologies are pivotal for their high data rate capabilities. However, their reliance on large antenna arrays and narrow directive beams for ensuring adequate receive signal power introduces significant beam training overheads. This becomes particularly challenging in supporting highly-mobile applications such as drone communication, where the dynamic nature of drones demands frequent beam alignment to maintain connectivity. Addressing this critical bottleneck, our paper introduces a novel machine learning-based framework that leverages multi-modal sensory data, including visual and positional information, to expedite and refine mmWave/THz beam prediction. Unlike conventional approaches that solely depend on exhaustive beam training methods, our solution incorporates additional layers…
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Taxonomy
TopicsUAV Applications and Optimization · Satellite Communication Systems · IoT and Edge/Fog Computing
MethodsGreedy Policy Search
