  Millimeter wave (mmWave) and terahertz (THz) drones have the potential to
enable several futuristic applications such as coverage extension, enhanced
security monitoring, and disaster management. However, these drones need to
deploy large antenna arrays and use narrow directive beams to maintain a
sufficient link budget. The large beam training overhead associated with these
arrays makes adjusting these narrow beams challenging for highly-mobile drones.
To address these challenges, this paper proposes a vision-aided machine
learning-based approach that leverages visual data collected from cameras
installed on the drones to enable fast and accurate beam prediction. Further,
to facilitate the evaluation of the proposed solution, we build a synthetic
drone communication dataset consisting of co-existing wireless and visual data.
The proposed vision-aided solution achieves a top-$1$ beam prediction accuracy
of $\approx 91\%$ and close to $100\%$ top-$3$ accuracy. These results
highlight the efficacy of the proposed solution towards enabling highly mobile
mmWave/THz drone communication.
