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