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This paper investigates a novel research direction that leverages vision tohelp overcome the critical wireless communication challenges. In particular,this paper considers millimeter wave (mmWave) communication systems, which areprincipal components of 5G and beyond. These systems face two importantchallenges: (i) the large training overhead associated with selecting theoptimal beam and (ii) the reliability challenge due to the high sensitivity tolink blockages. Interestingly, most of the devices that employ mmWave arrayswill likely also use cameras, such as 5G phones, self-driving vehicles, andvirtual/augmented reality headsets. Therefore, we investigate the potentialgains of employing cameras at the mmWave base stations and leveraging theirvisual data to help overcome the beam selection and blockage predictionchallenges. To do that, this paper exploits computer vision and deep learningtools to predict mmWave beams and blockages directly from the camera RGB imagesand the sub-6GHz channels. The experimental results reveal interesting insightsinto the effectiveness of such solutions. For example, the deep learning modelis capable of achieving over 90\% beam prediction accuracy, which only requiressnapping a shot of the scene and zero overhead.

Millimeter Wave Base Stations with Cameras: Vision Aided Beam and Blockage Prediction