  Beamforming techniques have been widely used in the millimeter wave (mmWave)
bands to mitigate the path loss of mmWave radio links as the narrow straight
beams by directionally concentrating the signal energy. However, traditional
mmWave beam management algorithms usually require excessive channel state
information overhead, leading to extremely high computational and communication
costs. This hinders the widespread deployment of mmWave communications. By
contrast, the revolutionary vision-assisted beam management system concept
employed at base stations (BSs) can select the optimal beam for the target user
equipment (UE) based on its location information determined by machine learning
(ML) algorithms applied to visual data, without requiring channel information.
In this paper, we present a comprehensive framework for a vision-assisted
mmWave beam management system, its typical deployment scenarios as well as the
specifics of the framework. Then, some of the challenges faced by this system
and their efficient solutions are discussed from the perspective of ML. Next, a
new simulation platform is conceived to provide both visual and wireless data
for model validation and performance evaluation. Our simulation results
indicate that the vision-assisted beam management is indeed attractive for
next-generation wireless systems.
