  Visual information, captured for example by cameras, can effectively reflect
the sizes and locations of the environmental scattering objects, and thereby
can be used to infer communications parameters like propagation directions,
receiver powers, as well as the blockage status. In this paper, we propose a
novel beam alignment framework that leverages images taken by cameras installed
at the mobile user. Specifically, we utilize 3D object detection techniques to
extract the size and location information of the dynamic vehicles around the
mobile user, and design a deep neural network (DNN) to infer the optimal beam
pair for transceivers without any pilot signal overhead. Moreover, to avoid
performing beam alignment too frequently or too slowly, a beam coherence time
(BCT) prediction method is developed based on the vision information. This can
effectively improve the transmission rate compared with the beam alignment
approach with the fixed BCT. Simulation results show that the proposed vision
based beam alignment methods outperform the existing LIDAR and vision based
solutions, and demand for much lower hardware cost and communication overhead.
