Unsupervised Clustering for Millimeter-Wave Channel Propagation Modeling

Published

February 11, 2018

Author(s)

Jian Wang, Camillo Gentile, Jelena Senic, Roy Sun, Peter B. Papazian

Abstract

We have designed and assembled millimeter-wave channel sounders at 28, 60, and 83 GHz. They can measure the three-dimensional (azimuth and elevation) double-directional angle (angle-of- departure and angle-of-arrival) of channel multipath components as well as their delay (with 0.5 ns resolution) and Doppler-frequency shift. In addition, because the receiver is mounted on a mobile robot, the systems can collect measurements for hundreds of different transmitter- receiver configurations in just minutes. Therefore channel-model reduction, including the multipath-component clustering step, must be reliable, consistent, and unsupervised. In this paper, we describe a simple clustering algorithm 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 measurements in five different environments, we show the algorithm to be robust and deliver consistent results across the three frequency bands.

Proceedings Title

2017 IEEE Vehicular Technology Conference - Fall

Conference Dates

September 24-27, 2017

Conference Location

Toronto, CA

Pub Type

Conferences

Download Paper

https://doi.org/10.1109/VTCFall.2017.8288377

Local Download

Keywords

5G, double-directional channel, mmWave

Citation

Wang, J. , Gentile, C. , Senic, J. , Sun, R. and Papazian, P. (2018), Unsupervised Clustering for Millimeter-Wave Channel Propagation Modeling, 2017 IEEE Vehicular Technology Conference - Fall, Toronto, CA, [online], https://doi.org/10.1109/VTCFall.2017.8288377, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=923603 (Accessed October 25, 2025)