{"title":"Wireless Devices Identification with Light-Weight Convolutional Neural Network Operating on Quadrant IQ Transition Image","authors":"Hiro Tamura, K. Yanagisawa, A. Shirane, K. Okada","doi":"10.1109/newcas49341.2020.9159777","DOIUrl":null,"url":null,"abstract":"This paper presents a wireless device identification method that uses a convolutional neural network (CNN) operating on a quadrant IQ transition image. The proposed method can identify IoT wireless devices by exploiting their RF fingerprints, which is a technology to identify wireless devices using variation in analog signals. We proposed a quadrant IQ image technique to reduce the size of CNN while maintaining the accuracy. The CNN utilizes the IQ transition image, which is cut out into four-part. The over-the-air measurement was performed with six Zigbee wireless devices to confirm the validity of the proposed identification method. The measurement results demonstrate that the proposed method can achieve 99% accuracy with the light-weight CNN model with 36,500 trainable parameters. Furthermore, the proposed threshold algorithm can realize the detection of unknown devices that are not trained with 80% accuracy for further secured wireless communication.","PeriodicalId":135163,"journal":{"name":"2020 18th IEEE International New Circuits and Systems Conference (NEWCAS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 18th IEEE International New Circuits and Systems Conference (NEWCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/newcas49341.2020.9159777","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
This paper presents a wireless device identification method that uses a convolutional neural network (CNN) operating on a quadrant IQ transition image. The proposed method can identify IoT wireless devices by exploiting their RF fingerprints, which is a technology to identify wireless devices using variation in analog signals. We proposed a quadrant IQ image technique to reduce the size of CNN while maintaining the accuracy. The CNN utilizes the IQ transition image, which is cut out into four-part. The over-the-air measurement was performed with six Zigbee wireless devices to confirm the validity of the proposed identification method. The measurement results demonstrate that the proposed method can achieve 99% accuracy with the light-weight CNN model with 36,500 trainable parameters. Furthermore, the proposed threshold algorithm can realize the detection of unknown devices that are not trained with 80% accuracy for further secured wireless communication.