Next Article in Journal
Development of a Smart Traceability System for the Rice Agroindustry Supply Chain in Indonesia
Next Article in Special Issue
Delay-Tolerant Sequential Decision Making for Task Offloading in Mobile Edge Computing Environments
Previous Article in Journal
Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network
Open AccessArticle

Clustering Algorithms and Validation Indices for a Wide mmWave Spectrum

Institute for the Wireless Internet of Things, Northeastern University, Boston, MA 02115, USA
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in the proceedings of Wireless Days 2019 and of our paper published in the proceedings of Wireless Telecommunications Symposium 2019 with investigations of the application of clustering techniques to a wide range of frequencies in the mmWave spectrum.
Information 2019, 10(9), 287; https://doi.org/10.3390/info10090287
Received: 16 August 2019 / Accepted: 10 September 2019 / Published: 19 September 2019
(This article belongs to the Special Issue Emerging Topics in Wireless Communications for Future Smart Cities)
Radio channel propagation models for the millimeter wave (mmWave) spectrum are extremely important for planning future 5G wireless communication systems. Transmitted radio signals are received as clusters of multipath rays. Identifying these clusters provides better spatial and temporal characteristics of the mmWave channel. This paper deals with the clustering process and its validation across a wide range of frequencies in the mmWave spectrum below 100 GHz. By way of simulations, we show that in outdoor communication scenarios clustering of received rays is influenced by the frequency of the transmitted signal. This demonstrates the sparse characteristic of the mmWave spectrum (i.e., we obtain a lower number of rays at the receiver for the same urban scenario). We use the well-known k-means clustering algorithm to group arriving rays at the receiver. The accuracy of this partitioning is studied with both cluster validity indices (CVIs) and score fusion techniques. Finally, we analyze how the clustering solution changes with narrower-beam antennas, and we provide a comparison of the cluster characteristics for different types of antennas. View Full-Text
Keywords: mmWave; clustering algorithms; cluster validity indices; channel propagation models mmWave; clustering algorithms; cluster validity indices; channel propagation models
Show Figures

Figure 1

MDPI and ACS Style

Antonescu, B.; Tehrani Moayyed, M.; Basagni, S. Clustering Algorithms and Validation Indices for a Wide mmWave Spectrum. Information 2019, 10, 287.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop