Radio Frequency Fingerprint Identification Based on Deep Complex Residual Network
Radio frequency fingerprint identification is a non-password authentication method based on the physical layer hardware of the communication device. Deep learning methods provide new ideas and techniques for radio frequency fingerprint identification. As a bridge between electromagnetic signal recog...
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doaj-2e4528ed9bd044ec9aa3144312a372022021-03-30T04:11:52ZengIEEEIEEE Access2169-35362020-01-01820441720442410.1109/ACCESS.2020.30372069253560Radio Frequency Fingerprint Identification Based on Deep Complex Residual NetworkShenhua Wang0Hongliang Jiang1Xiaofang Fang2Yulong Ying3https://orcid.org/0000-0002-3867-5893Jingchao Li4Bin Zhang5https://orcid.org/0000-0002-2577-6257State Grid Zhejiang Wuyi County Power Supply Company Ltd., Hangzhou, ChinaState Grid Zhejiang Wuyi County Power Supply Company Ltd., Hangzhou, ChinaState Grid Zhejiang Wuyi County Power Supply Company Ltd., Hangzhou, ChinaSchool of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai, ChinaSchool of Electronic and Information, Shanghai Dianji University, Shanghai, ChinaDepartment of Mechanical Engineering, Kanagawa University, Yokohama, JapanRadio frequency fingerprint identification is a non-password authentication method based on the physical layer hardware of the communication device. Deep learning methods provide new ideas and techniques for radio frequency fingerprint identification. As a bridge between electromagnetic signal recognition and deep learning, the electromagnetic signal recognition method based on statistical constellation still needs to go through data preprocessing and feature engineering, which is contrary to the end-to-end learning method emphasized by deep learning. Moreover, in the process of converting electromagnetic signal waveform data into images, there is inevitably information loss. Establishing a universal radio frequency fingerprint recognition model suitable for wireless communication scenarios is not only conducive to optimizing the communication system, but also can reduce the cost and time of model selection. Therefore, how to design a deep learning radio frequency fingerprint recognition model suitable for wireless communication is an important problem for researchers. Aiming at the problem that the existing radio frequency fingerprint extraction and identification methods have low recognition rate of communication radiation source individuals, a radio frequency fingerprint identification method based on deep complex residual network is proposed. Through the deep complex residual network, the radio frequency fingerprint feature extraction of the communication radiation source individual is integrated with the recognition process, and an end-to-end deep learning model suitable for wireless communication is established, which greatly improves the identification accuracy of the communication radiation source individuals compared with typical constellation based methods.https://ieeexplore.ieee.org/document/9253560/Wireless communicationradio frequency fingerprintconstellationdeep learningend-to-enddeep complex residual network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shenhua Wang Hongliang Jiang Xiaofang Fang Yulong Ying Jingchao Li Bin Zhang |
spellingShingle |
Shenhua Wang Hongliang Jiang Xiaofang Fang Yulong Ying Jingchao Li Bin Zhang Radio Frequency Fingerprint Identification Based on Deep Complex Residual Network IEEE Access Wireless communication radio frequency fingerprint constellation deep learning end-to-end deep complex residual network |
author_facet |
Shenhua Wang Hongliang Jiang Xiaofang Fang Yulong Ying Jingchao Li Bin Zhang |
author_sort |
Shenhua Wang |
title |
Radio Frequency Fingerprint Identification Based on Deep Complex Residual Network |
title_short |
Radio Frequency Fingerprint Identification Based on Deep Complex Residual Network |
title_full |
Radio Frequency Fingerprint Identification Based on Deep Complex Residual Network |
title_fullStr |
Radio Frequency Fingerprint Identification Based on Deep Complex Residual Network |
title_full_unstemmed |
Radio Frequency Fingerprint Identification Based on Deep Complex Residual Network |
title_sort |
radio frequency fingerprint identification based on deep complex residual network |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Radio frequency fingerprint identification is a non-password authentication method based on the physical layer hardware of the communication device. Deep learning methods provide new ideas and techniques for radio frequency fingerprint identification. As a bridge between electromagnetic signal recognition and deep learning, the electromagnetic signal recognition method based on statistical constellation still needs to go through data preprocessing and feature engineering, which is contrary to the end-to-end learning method emphasized by deep learning. Moreover, in the process of converting electromagnetic signal waveform data into images, there is inevitably information loss. Establishing a universal radio frequency fingerprint recognition model suitable for wireless communication scenarios is not only conducive to optimizing the communication system, but also can reduce the cost and time of model selection. Therefore, how to design a deep learning radio frequency fingerprint recognition model suitable for wireless communication is an important problem for researchers. Aiming at the problem that the existing radio frequency fingerprint extraction and identification methods have low recognition rate of communication radiation source individuals, a radio frequency fingerprint identification method based on deep complex residual network is proposed. Through the deep complex residual network, the radio frequency fingerprint feature extraction of the communication radiation source individual is integrated with the recognition process, and an end-to-end deep learning model suitable for wireless communication is established, which greatly improves the identification accuracy of the communication radiation source individuals compared with typical constellation based methods. |
topic |
Wireless communication radio frequency fingerprint constellation deep learning end-to-end deep complex residual network |
url |
https://ieeexplore.ieee.org/document/9253560/ |
work_keys_str_mv |
AT shenhuawang radiofrequencyfingerprintidentificationbasedondeepcomplexresidualnetwork AT hongliangjiang radiofrequencyfingerprintidentificationbasedondeepcomplexresidualnetwork AT xiaofangfang radiofrequencyfingerprintidentificationbasedondeepcomplexresidualnetwork AT yulongying radiofrequencyfingerprintidentificationbasedondeepcomplexresidualnetwork AT jingchaoli radiofrequencyfingerprintidentificationbasedondeepcomplexresidualnetwork AT binzhang radiofrequencyfingerprintidentificationbasedondeepcomplexresidualnetwork |
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