{"title":"基于象限IQ过渡图像的轻量级卷积神经网络无线设备识别","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":"{\"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}","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}
Wireless Devices Identification with Light-Weight Convolutional Neural Network Operating on Quadrant IQ Transition Image
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.