Millimeter-wave cellular communication requires beamforming procedures that enable alignment of the transmitter and receiver beams as the user equipment (UE) moves. For efficient beam tracking it is advantageous to classify users according to their traffic and mobility patterns. Research to date has demonstrated efficient ways of machine learning based UE classification. Although different machine learning approaches have shown success, most of them are based on physical layer attributes of the received signal. This, however, imposes additional complexity and requires access to those lower layer signals. In this paper, we show that traditional supervised and even unsupervised machine learning methods can successfully be applied on higher layer channel measurement reports in order to perform UE classification, thereby reducing the complexity of the classification process.

中文翻译：

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具有移动性的毫米波蜂窝系统中基于学习的 UE 分类

毫米波蜂窝通信需要波束成形程序，以便在用户设备 (UE) 移动时使发射器和接收器波束对齐。对于有效的波束跟踪，根据用户的流量和移动模式对用户进行分类是有利的。迄今为止的研究已经证明了基于机器学习的 UE 分类的有效方法。尽管不同的机器学习方法已经取得成功，但大多数都是基于接收信号的物理层属性。然而，这增加了额外的复杂性并且需要访问那些较低层的信号。在本文中，我们展示了传统的有监督甚至无监督机器学习方法可以成功地应用于高层信道测量报告，以执行 UE 分类，