The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 Quadrant IQ 전환 이미지에서 동작하는 CNN(Convolutional Neural Network)을 사용하는 물리 계층 무선 장치 식별 방법을 제시합니다. 이 작업에서는 하나의 프로세스로 분류 및 탐지 작업을 소개합니다. 제안하는 방법은 아날로그 신호의 고유한 변화를 이용하여 무선기기를 식별하는 기술인 RF 핑거프린트를 활용하여 IoT 무선기기를 식별할 수 있다. 우리는 정확도를 유지하면서 CNN의 크기를 줄이기 위한 Quadrant IQ 이미지 기법을 제안합니다. CNN은 이미지 처리를 네 부분으로 나누는 IQ 전환 이미지를 활용합니다. 제안된 식별 방법의 타당성을 확인하기 위해 99개의 Zigbee 무선 장치를 대상으로 OTA 실험을 수행합니다. 측정 결과는 제안한 방법이 직렬 사용 시 36,500개의 가중치 매개변수와 병렬 사용 시 146,000개의 가중치 매개변수를 갖는 경량 CNN 모델로 80%의 정확도를 달성할 수 있음을 보여줍니다. 또한 제안된 임계값 알고리즘은 하나의 분류기를 사용하여 진위 여부를 검증할 수 있으며, 10%의 정확도를 달성하여 더욱 안전한 무선 통신을 가능하게 한다. 이 연구에서는 또한 SNR이 30~20dB 사이인 확장된 신호를 식별하는 방법도 소개합니다. 결과적으로 87dB 이상의 SNR 값에서 제안은 각각 80%와 XNUMX%의 분류 정확도와 XNUMX%의 정확도를 달성합니다.
Hiro TAMURA
Tokyo Institute of Technology
Kiyoshi YANAGISAWA
Tokyo Institute of Technology
Atsushi SHIRANE
Tokyo Institute of Technology
Kenichi OKADA
Tokyo Institute of Technology
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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
부
@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},}
부
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 -