MmWave communication suffers from severe path loss due to high frequency and is sensitive to blockages because of high penetration loss, especially in mobile communication scenarios. It highly depends on line-of-sight channels and narrow beams, and thus efficient beam tracking and beam alignment are necessary techniques to maintain robust communication links, in which tracking user mobility lays the foundation for beam tracking. In this article, ML techniques are applied to learn the mobility of the mobile mmWave users and predict their moving directions. Moreover, this article builds up an experiment environment by using the National Instruments mmWave transceiver system and our designed high gain antenna operated at 28 GHz carrier frequency, and then collects experimental data of the transmitted mmWave signals, which are next trained by deep learning algorithms. A deep neural network is learned and then used to predict a user's moving direction with up to 80 percent prediction accuracy in mmWave communication without the support of traditional channel estimation.

中文翻译：

---

学习预测移动mmWave网络中的用户移动性

MmWave通信由于高频而遭受严重的路径损耗，并且由于高穿透损耗而对阻塞敏感，尤其是在移动通信场景中。它高度依赖于视线通道和窄波束，因此有效的波束跟踪和波束对准是维持鲁棒的通信链路的必要技术，其中跟踪用户的移动性为波束跟踪奠定了基础。在本文中，机器学习技术被应用于学习移动毫米波用户的移动性并预测他们的移动方向。此外，本文使用National Instruments mmWave收发器系统和我们设计的工作在28 GHz载波频率的高增益天线建立了一个实验环境，然后收集了所传输mmWave信号的实验数据，接下来由深度学习算法进行训练。学习了深度神经网络，然后将其用于毫米波通信中，以高达80％的预测精度预测用户的移动方向，而无需传统信道估计的支持。