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Unsupervised Clustering for Millimeter-Wave Channel Propagation Modeling



Jian Wang, Camillo 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, CA


5G, double-directional channel, mmWave


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],, (Accessed June 21, 2024)


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Created February 11, 2018, Updated April 19, 2022