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PUBLICATIONS

Temporal Exemplar Channels in High-Multipath Environments

Published

Author(s)

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

Abstract

Industrial wireless plays a crucial role in cyber-physical system (CPS) advances for the future vision of smart manufacturing. However, industrial wireless environments are different from each other and are different from home and office environments. Hence, industrial wireless channel modeling is essential for the development of industrial wireless systems. Moreover, millimeter-wave (mmWave) wireless bands have a high potential to be used for high data-rates required for industrial automation reliability, with multiple antennas envisioned to mitigate the high path loss. As a result, in this work, we introduce a machine learning based exemplar extraction approach on mmWave wireless spatial-channel measurements. The proposed approach processes the measured power-angle-delay-profiles to cluster them into a number of groups with respect to the angle of arrival. Then, an exemplar power-delay-profile (PDP) is extracted to represent each group. The resulting small number of exemplars is needed to represent the various cases that affect a wireless system in an environment and hence the approach is important for mmWave industrial wireless systems testing and evaluation.
Proceedings Title
2021 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021
Conference Dates
June 6-11, 2021
Conference Location
Toronto, Ontario,
Conference Title
2021 IEEE International Conference on Acoustics, Speech and Signal Processing
Pub Type
Conferences

Download Paper

https://doi.org/10.1109/ICASSP39728.2021.9414262
Local Download

Keywords

Channel modeling, clustering, exemplar channel, industrial wireless, unsupervised learning, wireless system
Wireless (RF) and Factory communications

Citation

Hany, M. , Vouras, P. , Jones, R. , Candell, R. and Remley, K. (2021), Temporal Exemplar Channels in High-Multipath Environments, 2021 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021, Toronto, Ontario, , [online], https://doi.org/10.1109/ICASSP39728.2021.9414262, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=931353 (Accessed April 24, 2024)

Created May 24, 2021