Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments
Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments
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DOI：
10.1109/gcwkshps56602.2022.10008524
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
G. Charan;A. Alkhateeb
中科院分区：
文献类型：
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作者：
G. Charan;A. Alkhateeb
本文首次对使用视觉(RGB摄像机)数据和机器学习在毫米波(MmWave)动态链路阻塞发生之前进行主动预测进行了真实世界的评估。主动预测视距(LOS)链路阻塞使毫米波/亚太赫兹网络能够在链路故障发生之前做出主动网络管理决策，例如主动波束切换和切换)。这可以在有效利用无线资源的同时显著提高网络可靠性和时延。为了在现实中评估这一收益，本文(I)开发了一种基于计算机视觉的解决方案，该解决方案处理安装在基础设施节点的摄像头捕获的视觉数据，以及(Ii)基于包含多模式传感和通信数据的大规模真实世界数据集DeepSense 6G研究所提出的解决方案的可行性。基于采用的真实数据集，所开发的解决方案在预测未来0内发生的堵塞方面达到了大约90%的准确率。1s内发生阻塞的成本约为$80%，这为毫米波/亚太赫兹通信网络提供了一种很有前途的解决方案。
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. 1s and $\approx$ 80% for blockages happening within 1s, which highlights a promising solution for mmWave/sub-THz communication networks.