LiDAR-Aided Mobile Blockage Prediction in Real-World Millimeter Wave Systems
LiDAR-Aided Mobile Blockage Prediction in Real-World Millimeter Wave Systems
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DOI：
10.1109/wcnc51071.2022.9771651
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Shunyao Wu;C. Chakrabarti;A. Alkhateeb
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文献类型：
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作者：
Shunyao Wu;C. Chakrabarti;A. Alkhateeb
视距链路阻塞是毫米波（mmWave）和太赫兹（THz）通信网络的可靠性和延迟的关键挑战。本文提出利用LiDAR传感数据来提供对通信环境的感知，并在动态链路阻塞发生之前主动预测它们。这允许网络对切换/波束切换做出主动决策，这增强了其可靠性和延迟。我们制定了激光雷达辅助阻塞预测问题，并提出了第一个现实世界的演示激光雷达辅助阻塞预测毫米波系统。特别是，我们基于DeepSense 6 G结构构建了一个大规模的真实世界数据集，其中包括户外车辆场景中共存的LiDAR和mmWave通信测量。然后，我们开发了一种有效的LiDAR数据去噪（静态聚类去除）算法和一种机器学习模型，可以主动预测动态链路阻塞。基于真实世界的数据集，我们的激光雷达辅助方法在预测100 ms内发生的堵塞时达到95%的准确率，在一秒内发生的堵塞预测准确率超过80%。如果用于主动切换，所提出的解决方案可以潜在地提供网络延迟的数量级节省，这突出了解决毫米波/亚太赫兹网络中的阻塞挑战的有希望的方向。
Line-of-sight link blockages represent a key challenge for the reliability and latency of millimeter wave (mmWave) and terahertz (THz) communication networks. This paper proposes to leverage LiDAR sensory data to provide awareness about the communication environment and proactively predict dynamic link blockages before they happen. This allows the network to make proactive decisions for hand-off/beam switching which enhances its reliability and latency. We formulate the LiDAR-aided blockage prediction problem and present the first real-world demonstration for LiDAR-aided blockage prediction in mmWave systems. In particular, we construct a large-scale real-world dataset, based on the DeepSense 6G structure, that comprises co-existing LiDAR and mmWave communication measurements in outdoor vehicular scenarios. Then, we develop an efficient LiDAR data denoising (static cluster removal) algorithm and a machine learning model that proactively predicts dynamic link blockages. Based on the real-world dataset, our LiDAR-aided approach is shown to achieve 95% accuracy in predicting blockages happening within 100ms and more than 80% prediction accuracy for blockages happening within one second. If used for proactive hand-off, the proposed solutions can potentially provide an order of magnitude saving in the network latency, which highlights a promising direction for addressing the blockage challenges in mmWave/sub-THz networks.