Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration
Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration
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DOI：
10.1109/ojcoms.2022.3162591
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发表时间：
2021-11
影响因子：
7.9
通讯作者：
Shunyao Wu;Muhammad Alrabeiah;C. Chakrabarti;A. Alkhateeb
中科院分区：
文献类型：
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作者：
Shunyao Wu;Muhammad Alrabeiah;C. Chakrabarti;A. Alkhateeb
克服链路阻塞挑战对于提高毫米波（mmWave）和亚太赫兹（sub-THz）通信网络的可靠性和延迟至关重要。以前的方法主要依赖于（i）多连接，其未充分利用网络资源，或者依赖于（ii）使用带外和非RF传感器来预测链路阻塞，这与增加的成本和系统复杂性相关联。在本文中，我们提出了一种新的解决方案，仅依赖于带内毫米波无线测量，以主动预测未来的动态视线（LOS）链路阻塞。所提出的解决方案利用深度神经网络和接收信号功率的特殊模式，我们称之为预阻塞无线签名来推断未来的阻塞。具体而言，开发的机器学习模型试图预测：（i）未来是否会发生堵塞？(ii)什么时候会发生这种堵塞？(iii)堵塞的类型是什么？以及（iv）运动的阻塞物的方向是什么？为了评估我们提出的方法，我们构建了一个大规模的真实世界数据集，包括室内和室外阻塞场景的近50万个数据点（毫米波测量）。使用该数据集的结果表明，所提出的方法可以成功预测未来动态堵塞的发生，准确率超过85%。此外，对于具有高度移动车辆阻塞的室外场景，所提出的模型可以预测未来阻塞的确切时间，对于未来600 ms内发生的阻塞，误差小于100 ms。这些结果突出了所提出的主动阻塞预测解决方案的有希望的收益，这可能会提高未来无线网络的可靠性和延迟。
Overcoming the link blockage challenges is essential for enhancing the reliability and latency of millimeter wave (mmWave) and sub-terahertz (sub-THz) communication networks. Previous approaches relied mainly on either (i) multiple-connectivity, which under-utilizes the network resources, or on (ii) the use of out-of-band and non-RF sensors to predict link blockages, which is associated with increased cost and system complexity. In this paper, we propose a novel solution that relies only on in-band mmWave wireless measurements to proactively predict future dynamic line of sight (LOS) link blockages. The proposed solution utilizes deep neural networks and special patterns of received signal power, that we call pre-blockage wireless signatures to infer future blockages. Specifically, the developed machine learning models attempt to predict: (i) If a future blockage will occur? (ii) When will this blockage happen? (iii) What is the type of the blockage? And (iv) what is the direction of the moving blockage? To evaluate our proposed approach, we build a large-scale real-world dataset comprising nearly 0.5 million data points (mmWave measurements) for both indoor and outdoor blockage scenarios. The results, using this dataset, show that the proposed approach can successfully predict the occurrence of future dynamic blockages with more than 85% accuracy. Further, for the outdoor scenario with highly-mobile vehicular blockages, the proposed model can predict the exact time of the future blockage with less than 100 ms error for blockages happening within the future 600 ms. These results, among others, highlight the promising gains of the proposed proactive blockage prediction solution which could potentially enhance the reliability and latency of future wireless networks.