  This paper investigates a novel research direction that leverages vision to
help overcome the critical wireless communication challenges. In particular,
this paper considers millimeter wave (mmWave) communication systems, which are
principal components of 5G and beyond. These systems face two important
challenges: (i) the large training overhead associated with selecting the
optimal beam and (ii) the reliability challenge due to the high sensitivity to
link blockages. Interestingly, most of the devices that employ mmWave arrays
will likely also use cameras, such as 5G phones, self-driving vehicles, and
virtual/augmented reality headsets. Therefore, we investigate the potential
gains of employing cameras at the mmWave base stations and leveraging their
visual data to help overcome the beam selection and blockage prediction
challenges. To do that, this paper exploits computer vision and deep learning
tools to predict mmWave beams and blockages directly from the camera RGB images
and the sub-6GHz channels. The experimental results reveal interesting insights
into the effectiveness of such solutions. For example, the deep learning model
is capable of achieving over 90\% beam prediction accuracy, which only requires
snapping a shot of the scene and zero overhead.
