  Beamforming technique realized by the multiple-input-multiple-output (MIMO)
antenna arrays has been widely used to compensate for the severe path loss in
the millimeter wave (mmWave) bands. In 5G NR system, the beam sweeping and beam
refinement are employed to find out the best beam codeword aligned to the
mobile. Due to the complicated handshaking and finite resolution of the
codebook, today's 5G-based beam management strategy is ineffective in various
scenarios in terms of the data rate, energy consumption, and also processing
latency. An aim of this article is to introduce a new type of beam management
framework based on the computer vision (CV) technique. In this framework
referred to as computer vision-aided beam management (CVBM), a camera attached
to the BS captures the image and then the deep learning-based object detector
identifies the 3D location of the mobile. Since the base station can directly
set the beam direction without codebook quantization and feedback delay, CVBM
achieves the significant beamforming gain and latency reduction. Using the
specially designed dataset called Vision Objects for Beam Management (VOBEM),
we demonstrate that CVBM achieves more than 40% improvement in the beamforming
gain and 40% reduction in the beam training overhead over the 5G NR beam
management.
