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

Gouranga Charan, Ahmed Alkhateeb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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 90% accuracy in predicting blockages happening within the future 0. 1s and 80% for blockages happening within 1s, which highlights a promising solution for mmWave/sub-THz communication networks.

Original languageEnglish (US)
Title of host publication2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1711-1716
Number of pages6
ISBN (Electronic)9781665459754
DOIs
StatePublished - 2022
Event2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Virtual, Online, Brazil
Duration: Dec 4 2022Dec 8 2022

Publication series

Name2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings

Conference

Conference2022 IEEE GLOBECOM Workshops, GC Wkshps 2022
Country/TerritoryBrazil
CityVirtual, Online
Period12/4/2212/8/22

Keywords

  • blockage prediction
  • computer vision
  • deep learning
  • mmWave
  • terahertz.

ASJC Scopus subject areas

  • Instrumentation
  • Computer Networks and Communications
  • Control and Optimization

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