Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures
Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures
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DOI：
10.1109/ojcoms.2023.3239434
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发表时间：
2022-11
影响因子：
7.9
通讯作者：
Shunyao Wu;C. Chakrabarti;A. Alkhateeb
中科院分区：
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。为了应对这一挑战，本文利用mmWave和LiDAR传感数据来提供对通信环境的感知，并在发生动态链路阻塞之前主动预测它们。这使得网络能够主动做出切换/波束切换的决策，从而提高网络可靠性和延迟。更具体地说，本文解决了以下关键问题：（i）我们可以预测视线链路阻塞，在它发生之前，使用带内毫米波/太赫兹信号和激光雷达传感数据？(ii)我们还能预测这种阻塞何时发生吗？(iii)我们能预测阻塞持续时间吗？以及（iv）我们能预测移动障碍物的方向吗？为此，我们开发了机器学习解决方案，可以学习接收到的信号和传感数据的特殊模式，我们称之为阻塞前签名，以推断未来的阻塞。为了评估所提出的方法，我们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，我们开发了一种高效的LiDAR数据去噪算法，该算法对LiDAR数据进行了一些预处理。基于真实世界的数据集，所开发的方法被证明在预测100 ms内发生的阻塞时达到95%以上的准确度，并且在一秒内发生的阻塞的预测准确度超过80%。鉴于这种未来的阻塞预测能力，本文还表明，开发的解决方案可以实现一个数量级的节省网络延迟，这进一步突出了开发的阻塞预测解决方案的无线网络的潜力。
Line-of-sight link blockages represent a key challenge for the reliability and latency of millimeter wave (mmWave) and terahertz (THz) communication networks. To address this challenge, this paper leverages mmWave and LiDAR sensory data to provide awareness about the communication environment and proactively predict dynamic link blockages before they occur. This allows the network to make proactive decisions for hand-off/beam switching, enhancing the network reliability and latency. More specifically, this paper addresses the following key questions: (i) Can we predict a line-of-sight link blockage, before it happens, using in-band mmWave/THz signal and LiDAR sensing data? (ii) Can we also predict when this blockage will occur? (iii) Can we predict the blockage duration? And (iv) can we predict the direction of the moving blockage? For that, we develop machine learning solutions that learn special patterns of the received signal and sensory data, which we call pre-blockage signatures, to infer future blockages. To evaluate the proposed approaches, we build a large-scale real-world dataset that comprises co-existing LiDAR and mmWave communication measurements in outdoor vehicular scenarios. Then, we develop an efficient LiDAR data denoising algorithm that applies some pre-processing to the LiDAR data. Based on the real-world dataset, the developed approaches are shown to achieve above 95% accuracy in predicting blockages occurring within 100 ms and more than 80% prediction accuracy for blockages occurring within one second. Given this future blockage prediction capability, the paper also shows that the developed solutions can achieve an order of magnitude saving in network latency, which further highlights the potential of the developed blockage prediction solutions for wireless networks.