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

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 ha...

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Published inIEEE open journal of instrumentation and measurement Vol. 1; pp. 1 - 15
Main Authors Kashef, Mohamed, Vouras, Peter, Jones, Robert D., Candell, Richard, Remley, Kate A.
Format Journal Article
LanguageEnglish
Published IEEE 2022
Subjects
Apertures
Channel modeling
clustering
Delays
exemplar channel
Feature extraction
Industrial Internet of Things
mmWave channels
NIST
unsupervised learning
Wireless communication
Wireless sensor networks
wireless systems
Online AccessGet full text
ISSN2768-7236
2768-7236
DOI10.1109/OJIM.2022.3181309

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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.
AbstractList 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.
Author Candell, Richard
Remley, Kate A.
Jones, Robert D.
Kashef, Mohamed
Vouras, Peter
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Snippet Industrial wireless channel modeling is essential for the development of Industrial Internet of Things (IIoT) wireless systems. Moreover, millimeter-wave...
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SubjectTerms Apertures
Channel modeling
clustering
Delays
exemplar channel
Feature extraction
Industrial Internet of Things
mmWave channels
NIST
unsupervised learning
Wireless communication
Wireless sensor networks
wireless systems
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Title A Machine-Learning Approach for the Exemplar Extraction of mmWave Industrial Wireless Channels
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