{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/computer-vision-aided-blockage-prediction-in","title":"Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments","arxiv_id":"2203.01907","date":"2022-03-03","proceeding":null,"authors":["Gouranga Charan","Ahmed Alkhateeb"],"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.","url_abs":"https://arxiv.org/abs/2203.01907v1","url_pdf":"https://arxiv.org/pdf/2203.01907v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"computer-vision-aided-blockage-prediction-in","repo_url":"https://github.com/gourangc/Vision_Aided_Blockage_prediction","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}