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

arXiv: https://arxiv.org/abs/2203.01907 · HTML: https://mlsanity.com/paper/2203.01907/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://mlsanity.com/paper/1910.02900/) (2019)
- [Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/2102.09527/) (2021)
- [Machine Learning for Reliable mmWave Systems: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/1807.02723/) (2018)
- [Radar Aided Proactive Blockage Prediction in Real-World Millimeter Wave Systems](https://mlsanity.com/paper/2111.14805/) (2021)
- [Improved Handover Through Dual Connectivity in 5G mmWave Mobile Networks](https://mlsanity.com/paper/1611.04748/) (2016)
- [Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration](https://mlsanity.com/paper/2111.08242/) (2021)
- [LiDAR-Aided Mobile Blockage Prediction in Real-World Millimeter Wave Systems](https://mlsanity.com/paper/2111.09581/) (2021)

## Built on by (most cited first, 9 of 9)

- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [DeepSense-V2V: A Vehicle-to-Vehicle Multi-Modal Sensing, Localization, and Communications Dataset](https://mlsanity.com/paper/2406.17908/) (2024)
- [Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access](https://mlsanity.com/paper/2312.13931/) (2023)
- [Environment-Aware Channel Inference via Cross-Modal Flow: From Multimodal Sensing to Wireless Channels](https://mlsanity.com/paper/2512.04966/) (2025)
- [Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey](https://mlsanity.com/paper/2601.03181/) (2026)
- [Multi-modal Image and Radio Frequency Fusion for Optimizing Vehicle Positioning](https://mlsanity.com/paper/2410.19788/) (2024)
- [Multi-modal Data based Semi-Supervised Learning for Vehicle Positioning](https://mlsanity.com/paper/2410.20680/) (2024)
- [Vision Aided Channel Prediction for Vehicular Communications: A Case Study of Received Power Prediction Using RGB Images](https://mlsanity.com/paper/2501.18618/) (2025)
- [CSIYOLO: An Intelligent CSI-based Scatter Sensing Framework for Integrated Sensing and Communication Systems](https://mlsanity.com/paper/2509.19335/) (2025)

## Abstract (arXiv)

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.