  In this paper, we present a novel framework of 3D scene based beam selection
for mmWave communications that relies only on the environmental data and deep
learning techniques. Different from other out-of-band side-information aided
communication strategies, the proposed one fully utilizes the environmental
information, e.g., the shape, the position, and even the materials of the
surrounding buildings/cars/trees that are obtained from 3D scene
reconstruction. Specifically, we build the neural networks with the input as
point cloud of the 3D scene and the output as the beam indices. Compared with
the LIDAR aided technique, the reconstructed 3D scene here is achieved from
multiple images taken offline from cameras and thus significantly lowers down
the cost and makes itself applicable for small mobile terminals. Simulation
results show that the proposed 3D scene based beam selection can outperform the
LIDAR method in terms of accuracy.
