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PUBLICATIONS

A Machine-Learning Approach for the Exemplar Extraction of mmWave Industrial Wireless Channels

Published

Author(s)

Mohamed Hany, Peter Vouras, Rob Jones, Rick Candell, Kate Remley

Abstract

Industrial wireless channel modeling is essential for the development of Industrial Internet of Things (IIoT) wireless systems. Moreover, millimeter-wave (mmWave) wireless bands have a high potential to be used for IIoT applications because of their high data-rates and the better applicability of having multiple antennas. As a result, we introduce an exemplar extraction approach to be applied on mmWave wireless channel measurements. A machine learning (ML) clustering scheme is used to divide the measured power-angle-delay-profiles into a number of groups with respect to the angle of arrival. Each of the groups is represented by a power-delay-profile (PDP) exemplar to provide a tractable way for testing and evaluation of mmWave IIoT wireless systems through compactly representing different groups based on their spatial characteristics. Hence, testing of wireless communications equipment can be performed over the exemplars to assess their spatial performance with a significantly reduced amount of data, allowing the development of lab-based device evaluation in a realistic, yet repeatable, test environment. Governing equations are provided in sufficient detail for users to implement the technique in their own labs.
Citation
IEEE Open Journal of Instrumentation and Measurement
Volume
1
Pub Type
Journals

Download Paper

https://doi.org/10.1109/OJIM.2022.3181309
Local Download

Keywords

Channel modeling, mmWave channels, clustering, exemplar channel, unsupervised learning, wireless systems
Wireless (RF), Factory communications and Advanced communications

Citation

Hany, M. , Vouras, P. , Jones, R. , Candell, R. and Remley, K. (2022), A Machine-Learning Approach for the Exemplar Extraction of mmWave Industrial Wireless Channels, IEEE Open Journal of Instrumentation and Measurement, [online], https://doi.org/10.1109/OJIM.2022.3181309, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933168 (Accessed April 23, 2024)

Created June 13, 2022, Updated November 29, 2022