Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments

TL;DR

This paper demonstrates a real-world computer vision approach using RGB camera data and machine learning to predict millimeter wave link blockages proactively, significantly improving network reliability and resource management.

Contribution

It introduces a novel vision-based blockage prediction method evaluated on a large-scale real-world dataset, achieving high accuracy within short future timeframes.

Findings

Achieves approximately 90% accuracy for 0.1s ahead blockage prediction.

Achieves approximately 80% accuracy for 1s ahead blockage prediction.

Validates the feasibility of vision-based proactive blockage prediction in real-world mmWave networks.

Abstract

This paper provides the first real-world evaluation of using visual (RGB camera) data and machine learning for proactively predicting millimeter wave (mmWave) dynamic link blockages before they happen. Proactively predicting line-of-sight (LOS) link blockages enables mmWave/sub-THz networks to make proactive network management decisions, such as proactive beam switching and hand-off) before a link failure happens. This can significantly enhance the network reliability and latency while efficiently utilizing the wireless resources. To evaluate this gain in reality, this paper (i) develops a computer vision based solution that processes the visual data captured by a camera installed at the infrastructure node and (ii) studies the feasibility of the proposed solution based on the large-scale real-world dataset, DeepSense 6G, that comprises multi-modal sensing and communication data. Based on the adopted real-world dataset, the developed solution achieves $\approx 90\%$ accuracy in predicting blockages happening within the future $0.1$s and $\approx 80\%$ for blockages happening within $1$s, which highlights a promising solution for mmWave/sub-THz communication networks.