论文标题
主动使用LiDAR和带内标志来主动预测动态6G链接阻塞
Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures
论文作者
论文摘要
视线链接阻塞代表了毫米波（MMWave）和Terahertz（THZ）通信网络的可靠性和潜伏期的关键挑战。为了应对这一挑战，本文利用MMWave和激光雷达感官数据来提供对通信环境的认识，并在发生之前主动预测动态链接阻塞。这使网络可以主动做出交接/梁开关的决定，从而增强网络的可靠性和延迟。更具体地说，本文解决了以下关键问题：（i）我们可以在使用频段内MMWAVE/THZ信号和LIDAR感应数据之前预测链接链接阻塞在发生之前吗？ （ii）我们还可以预测何时发生这种阻塞吗？ （iii）我们可以预测阻塞持续时间吗？ （iv）我们可以预测移动阻塞的方向吗？为此，我们开发了机器学习解决方案，这些解决方案可以学习接收的信号和感官数据的特殊模式，我们称之为\ textit {preblockage签名}，以推断未来的障碍。为了评估所提出的方法，我们构建了一个大规模的现实数据集，该数据集包括在户外车队场景中共存的LIDAR和MMWAVE通信测量值。然后，我们开发了一种有效的LiDAR数据降解算法，该算法将某些预处理应用于LiDAR数据。根据现实世界数据集，在预测100毫秒内发生的堵塞和超过80 \％的预测准确性，在一秒钟内发生的堵塞物的预测准确性超过80 \％的预测准确性，显示出开发的方法可达到95 \％的准确性。鉴于这一未来的阻塞预测能力，本文还表明，开发的解决方案可以在网络延迟中节省数量级，这进一步突出了无线网络开发的阻塞预测解决方案的潜力。
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 \textit{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.
