Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming

TL;DR

This paper proposes a computer vision-based beam management framework for 6G millimeter and terahertz communications, significantly improving beamforming gain and reducing training overhead by directly estimating user location with deep learning.

Contribution

It introduces CVBM, a novel framework using camera images and deep learning for direct beam direction setting, bypassing traditional codebook and feedback methods.

Findings

Achieves over 40% improvement in beamforming gain.

Reduces beam training overhead by 40%.

Demonstrates effectiveness with the VOBEM dataset.

Abstract

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.