Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction
This addresses the problem of maintaining communication links for highly mobile drones in applications like coverage extension and disaster management, representing an incremental improvement over existing methods.
The paper tackles the challenge of high beam training overhead for millimeter wave and terahertz drones by proposing a vision-aided machine learning approach that uses camera data for fast beam prediction, achieving approximately 91% top-1 and nearly 100% top-3 accuracy.
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.