{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T11:33:01Z","timestamp":1722943981413},"reference-count":24,"publisher":"Engineering and Technology Publishing","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["jcm"],"published-print":{"date-parts":[[2021]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.12720\/jcm.16.7.276-283","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T07:51:00Z","timestamp":1634716260000},"page":"276-283","source":"Crossref","is-referenced-by-count":7,"title":["WLAN Interference Identification Using a Convolutional Neural Network for Factory Environments"],"prefix":"10.12720","author":[{"name":"Wave Engineering Laboratories, Advanced Telecommunications Research Institute International (ATR), 2-2-2, Hikaridai, Soraku, Seika, Kyoto, 619-0288, Japan","sequence":"first","affiliation":[]},{"given":"Julian","family":"Webber","sequence":"first","affiliation":[]},{"given":"Kazuto","family":"Yano","sequence":"additional","affiliation":[]},{"given":"Norisato","family":"Suga","sequence":"additional","affiliation":[]},{"given":"Yafei","family":"Hou","sequence":"additional","affiliation":[]},{"given":"Eiji","family":"Nii","sequence":"additional","affiliation":[]},{"given":"Toshihide","family":"Higashimori","sequence":"additional","affiliation":[]},{"given":"Abolfazl","family":"Mehbodniya","sequence":"additional","affiliation":[]},{"given":"Yoshinori","family":"Suzuki","sequence":"additional","affiliation":[]}],"member":"4977","published-online":{"date-parts":[[2021]]},"reference":[{"key":"ref0","doi-asserted-by":"publisher","unstructured":"[1] M. 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