Unsupervised Clustering for Millimeter-Wave Channel Propagation Modeling

Published: February 12, 2018


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


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, -1
Pub Type: Conferences


5G, double-directional channel, mmWave
Created February 12, 2018, Updated November 10, 2018