In this paper, we consider millimeter Wave (mmWave) technology to provide reliable wireless network service within factories where links may experience rapid and temporary fluctuations of the received signal power due to dynamic blockers, such as humans and robots, moving in the environment. We propose a novel beam recovery procedure that leverages Machine Learning (ML) tools to predict the starting and finishing of blockage events. This erases the delay introduced by current 5G New Radio (5G-NR) procedures when switching to an alternative serving base station and beam, and then re-establish the primary connection after the blocker has moved away. Firstly, we generate synthetic data using a detailed system-level simulator that integrates the most recent 3GPP 3D Indoor channel models and the geometric blockage Model-B. Then, we use the generated data to train offline a set of beam-specific Deep Neural Network (DNN) models that provide predictions about the beams' blockage states. Finally, we deploy the DNN models online into the system-level simulator to evaluate the benefits of the proposed solution. Our prediction-based beam recovery procedure guarantee higher signal level stability and up to $82\%$ data rate improvement with respect detection-based methods when blockers move at speed of $2$ m/s.

翻译：在本文中,我们考虑毫米波(mmWave)技术,以便在各工厂内提供可靠的无线网络服务,因为各工厂的连接可能因动态阻塞器(如人类和机器人)而导致接收信号能量迅速和暂时波动,从而在环境中移动。我们提出一个新的波束回收程序,利用机器学习(ML)工具来预测阻塞事件的开始和结束。这消除了目前5G新无线电(5G-NR)程序在转换到替代基站和波束时出现的延迟,并在阻塞器移动后重新建立主连接。首先,我们使用一个详细的系统级模拟器生成合成数据,该模拟器将最新的3GPP 3D室内通道模型和几何制阻隔模型纳入其中。然后,我们利用生成的数据将一组特定光谱的深神经网络(DNNN)模型(DNN)模型用于预测波段的阻塞状态。最后,我们将DNN模型在线安装到系统级模拟器上,以评估拟议解决方案的效益。首先,我们使用一个系统级模拟器生成合成数据模拟器,将最新的3GPP 3D 3D 室内频道频道模拟器模型模型模型,然后将数据稳定度提升到8- 以 信号检测速度测量数据。