IEICE TRANSACTIONS on Communications
Performance Evaluation of Classification and Verification with Quadrant IQ Transition Image
Summary :
This paper presents a physical layer wireless device identification method that uses a convolutional neural network (CNN) operating on a quadrant IQ transition image. This work introduces classification and detection tasks in one process. The proposed method can identify IoT wireless devices by exploiting their RF fingerprints, a technology to identify wireless devices by using unique variations in analog signals. We propose a quadrant IQ image technique to reduce the size of CNN while maintaining accuracy. The CNN utilizes the IQ transition image, which image processing cut out into four-part. An over-the-air experiment is performed on six Zigbee wireless devices to confirm the proposed identification method's validity. The measurement results demonstrate that the proposed method can achieve 99% accuracy with the light-weight CNN model with 36,500 weight parameters in serial use and 146,000 in parallel use. Furthermore, the proposed threshold algorithm can verify the authenticity using one classifier and achieved 80% accuracy for further secured wireless communication. This work also introduces the identification of expanded signals with SNR between 10 to 30dB. As a result, at SNR values above 20dB, the proposals achieve classification and detection accuracies of 87% and 80%, respectively.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E105-B No.5 pp.580-587
- Publication Date
- 2022/05/01
- Publicized
- 2021/12/01
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2021EBP3087
- Type of Manuscript
- PAPER
- Category
- Network Management/Operation
Authors
Hiro TAMURA
Tokyo Institute of Technology
Kiyoshi YANAGISAWA
Tokyo Institute of Technology
Atsushi SHIRANE
Tokyo Institute of Technology
Kenichi OKADA
Tokyo Institute of Technology
Keyword
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Hiro TAMURA, Kiyoshi YANAGISAWA, Atsushi SHIRANE, Kenichi OKADA, "Performance Evaluation of Classification and Verification with Quadrant IQ Transition Image" in IEICE TRANSACTIONS on Communications,
vol. E105-B, no. 5, pp. 580-587, May 2022, doi: 10.1587/transcom.2021EBP3087.
Abstract: This paper presents a physical layer wireless device identification method that uses a convolutional neural network (CNN) operating on a quadrant IQ transition image. This work introduces classification and detection tasks in one process. The proposed method can identify IoT wireless devices by exploiting their RF fingerprints, a technology to identify wireless devices by using unique variations in analog signals. We propose a quadrant IQ image technique to reduce the size of CNN while maintaining accuracy. The CNN utilizes the IQ transition image, which image processing cut out into four-part. An over-the-air experiment is performed on six Zigbee wireless devices to confirm the proposed identification method's validity. The measurement results demonstrate that the proposed method can achieve 99% accuracy with the light-weight CNN model with 36,500 weight parameters in serial use and 146,000 in parallel use. Furthermore, the proposed threshold algorithm can verify the authenticity using one classifier and achieved 80% accuracy for further secured wireless communication. This work also introduces the identification of expanded signals with SNR between 10 to 30dB. As a result, at SNR values above 20dB, the proposals achieve classification and detection accuracies of 87% and 80%, respectively.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021EBP3087/_p
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@ARTICLE{e105-b_5_580,
author={Hiro TAMURA, Kiyoshi YANAGISAWA, Atsushi SHIRANE, Kenichi OKADA, },
journal={IEICE TRANSACTIONS on Communications},
title={Performance Evaluation of Classification and Verification with Quadrant IQ Transition Image},
year={2022},
volume={E105-B},
number={5},
pages={580-587},
abstract={This paper presents a physical layer wireless device identification method that uses a convolutional neural network (CNN) operating on a quadrant IQ transition image. This work introduces classification and detection tasks in one process. The proposed method can identify IoT wireless devices by exploiting their RF fingerprints, a technology to identify wireless devices by using unique variations in analog signals. We propose a quadrant IQ image technique to reduce the size of CNN while maintaining accuracy. The CNN utilizes the IQ transition image, which image processing cut out into four-part. An over-the-air experiment is performed on six Zigbee wireless devices to confirm the proposed identification method's validity. The measurement results demonstrate that the proposed method can achieve 99% accuracy with the light-weight CNN model with 36,500 weight parameters in serial use and 146,000 in parallel use. Furthermore, the proposed threshold algorithm can verify the authenticity using one classifier and achieved 80% accuracy for further secured wireless communication. This work also introduces the identification of expanded signals with SNR between 10 to 30dB. As a result, at SNR values above 20dB, the proposals achieve classification and detection accuracies of 87% and 80%, respectively.},
keywords={},
doi={10.1587/transcom.2021EBP3087},
ISSN={1745-1345},
month={May},}
Copy
TY - JOUR
TI - Performance Evaluation of Classification and Verification with Quadrant IQ Transition Image
T2 - IEICE TRANSACTIONS on Communications
SP - 580
EP - 587
AU - Hiro TAMURA
AU - Kiyoshi YANAGISAWA
AU - Atsushi SHIRANE
AU - Kenichi OKADA
PY - 2022
DO - 10.1587/transcom.2021EBP3087
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E105-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2022
AB - This paper presents a physical layer wireless device identification method that uses a convolutional neural network (CNN) operating on a quadrant IQ transition image. This work introduces classification and detection tasks in one process. The proposed method can identify IoT wireless devices by exploiting their RF fingerprints, a technology to identify wireless devices by using unique variations in analog signals. We propose a quadrant IQ image technique to reduce the size of CNN while maintaining accuracy. The CNN utilizes the IQ transition image, which image processing cut out into four-part. An over-the-air experiment is performed on six Zigbee wireless devices to confirm the proposed identification method's validity. The measurement results demonstrate that the proposed method can achieve 99% accuracy with the light-weight CNN model with 36,500 weight parameters in serial use and 146,000 in parallel use. Furthermore, the proposed threshold algorithm can verify the authenticity using one classifier and achieved 80% accuracy for further secured wireless communication. This work also introduces the identification of expanded signals with SNR between 10 to 30dB. As a result, at SNR values above 20dB, the proposals achieve classification and detection accuracies of 87% and 80%, respectively.
ER -
English
