论文标题
现实世界中的计算机视觉辅助阻塞预测
Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments
论文作者
论文摘要
本文提供了对使用Visual（RGB摄像头）数据和机器学习的首次真实评估，以主动预测毫米波（MMWave）动态链接阻塞。主动预测视线线（LOS）链接阻塞使MMWave/Sub-THZ网络能够在链接失败发生之前做出主动网络管理决策，例如主动的束开关和交接）。这可以显着提高网络的可靠性和延迟，同时有效地利用无线资源。为了评估这一收益，本文（i）开发了一种基于计算机视觉的解决方案，该解决方案处理由安装在基础设施节点上的摄像机捕获的视觉数据，并且（ii）研究了基于大规模真实世界数据集（Deepsense 6G）的拟议解决方案的可行性，该数据集构成了多模式传感和通信数据。根据采用的现实世界数据集，开发的解决方案在预测将来发生的$ 0.1 $ s和$ \％$ $ $ \％$ $ $ 1 $ s的$ 1 $ s内实现了约90 \％$的精度，这突出了MMWave/sub-thz通信网络的有前途的解决方案。
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.
