  Enabling highly-mobile millimeter wave (mmWave) and terahertz (THz) wireless
communication applications requires overcoming the critical challenges
associated with the large antenna arrays deployed at these systems. In
particular, adjusting the narrow beams of these antenna arrays typically incurs
high beam training overhead that scales with the number of antennas. To address
these challenges, this paper proposes a multi-modal machine learning based
approach that leverages positional and visual (camera) data collected from the
wireless communication environment for fast beam prediction. The developed
framework has been tested on a real-world vehicular dataset comprising
practical GPS, camera, and mmWave beam training data. The results show the
proposed approach achieves more than $\approx$ 75\% top-1 beam prediction
accuracy and close to 100\% top-3 beam prediction accuracy in realistic
communication scenarios.
