Unsupervised Clustering for Millimeter-Wave Channel Propagation Modeling

Jian Wang, C. Gentile, Jelena Senic, Ruoyu Sun, P. Papazian, Chiehping Lai
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引用次数: 6

Abstract

To date, we have designed and assembled millimeter-wave channel sounders at 60 GHz and 83 GHz. They can estimate the angle-of-departure and angle-of-arrival of channel multipath components as well as their delay and Doppler frequency shift. In addition, due to the fast acquisition time and because the receiver is mounted on a mobile robot, the systems can collect measurements for hundreds of different transmitter-receiver configurations in just minutes. It follows that channel-model reduction, including the multipath- component clustering process, must be reliable, consistent, and unsupervised. In this paper, we describe a simple clustering process tailored to the properties of millimeter-wave channels that fully exploits the multi-dimensionality of the extracted multipath components and requires only a few tunable parameters. Through extensive experimentation, we have verified that the process is robust and delivers consistent results across five different environments and across both frequency bands investigated. Illustrative examples are provided.
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毫米波信道传播建模的无监督聚类
迄今为止,我们已经设计和组装了60 GHz和83 GHz的毫米波信道测深仪。它们可以估计信道多径分量的出发角和到达角,以及它们的延迟和多普勒频移。此外,由于采集时间短,并且接收器安装在移动机器人上,该系统可以在几分钟内收集数百种不同的收发配置的测量数据。因此,信道模型缩减,包括多路径组件聚类过程,必须是可靠的、一致的和无监督的。在本文中,我们描述了一种针对毫米波信道特性的简单聚类过程,该过程充分利用了提取的多径分量的多维性,并且只需要几个可调参数。通过广泛的实验,我们已经验证了该过程的鲁棒性,并在五种不同的环境和所调查的两个频段中提供一致的结果。提供了说明性示例。
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