A Computer Vision Based Beamforming Scheme for Millimeter Wave Communication in LOS Scenarios

TL;DR

This paper introduces a vision-based beamforming scheme for millimeter wave communication in LOS scenarios, leveraging CNN-derived position data from camera images to enhance coverage and privacy.

Contribution

It presents a novel location-aware beamforming method using computer vision and CNNs, enabling effective beam steering with privacy protection in low mobility LOS environments.

Findings

CNN achieves accurate positioning with low latency

Privacy-preserving low-resolution videos are effective

Beamforming improves coverage in LOS scenarios

Abstract

A novel location-aware beamforming scheme for millimeter wave communication is proposed for line of sight (LOS) and low mobility scenarios, in which computer vision is introduced to derive the required position or spatial angular information from the image or video captured by camera(s) co-located with mmWave antenna array at base stations. A wireless coverage model is built to investigate the coverage performance and influence of positioning accuracy achieved by convolutional neural network (CNN) for image processing. In addition, videos could be intentionally blurred, or even low-resolution videos could be directly applied, to protect users' privacy with acceptable positioning precision, lower computation complexity and lower camera cost. It is proved by simulations that the beamforming scheme is practicable and the mainstream CNN we employed is sufficient in both aspects of beam directivity accuracy and processing speed in frame per second.