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

• URL: http://arxiv.org/abs/2203.01907v1
• Date: Thu, 3 Mar 2022 18:38:10 GMT
• Title: Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments
• Authors: Gouranga Charan and Ahmed Alkhateeb
• Abstract summary: This paper develops a computer vision based solution that processes the visual data captured by a camera installed at the infrastructure node. Based on the adopted real-world dataset, the developed solution achieves $approx 90%$ accuracy in predicting blockages happening within the future.
• Score: 11.842197872454848
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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