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WLAN Interference Identification Using a Convolutional Neural Network for Factory Environments

Julian Webber 1,2, Kazuto Yano 1, Norisato Suga 1,3, Yafei Hou 1,4, Eiji Nii 1, Toshihide Higashimori 1, Abolfazl Mehbodniya1,5, and Yoshinori Suzuki1
1. Wave Engineering Laboratories, Advanced Telecommunications Research Institute International (ATR), 2-2-2, Hikaridai, Soraku, Seika, Kyoto, 619-0288, Japan
2. Graduate School of Engineering Science, Osaka University, Toyonaka, Japan
3. Faculty of Engineering, Tokyo University of Science, Tokyo, Japan
4. Graduate School of Natural Science and Technology, Okayama University, Okayama City, Japan
5. Kuwait College of Science and Technology, Kuwait

Abstract—Factory communication systems require highly re- liable links with predictable performance and quality of service in order to avoid outages that can damage the production-line process. Communication anomalies can be caused by narrowband interference which is difficult to identify and track from the time- domain information only. This paper describes a methodology for classifying increasing severity and types of interference in order to improve throughput prediction. Received signal strength (RSS) data is collected from both a ray-tracing simulation and a Wireless Local Area Network (WLAN) measurement campaign with a transmitter mounted on an actual automated guided vehicle (AGV). Scalogram time-frequency images are computed from the RSS signal and a convolutional neural network (CNN) is then trained to recognize the spectral features and enable the interference classification. The block random interference could be correctly classified on over 65% of the occasions in the ray- traced channel at 30 dB SNR.
Index Terms—WLAN, CNN, deep-learning, interference, scalogram, ray-tracing, factory communications

Cite: Julian Webber, Kazuto Yano, Norisato Suga, Yafei Hou, Eiji Nii, Toshihide Higashimori, Abolfazl Mehbodniya, and Yoshinori Suzuki, "WLAN Interference Identification Using a Convolutional Neural Network for Factory Environments," Journal of Communications vol. 16, no. 7, pp. 276-283, July 2021. Doi: 10.12720/jcm.16.7.276-283

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.