High-efficiency IQ convolutional network structure for radio frequency fingerprint identification

doi:10.11887/j.cn.202204020

Home > Archive>Volume 44, Issue 4, 2022 >180-189. DOI:10.11887/j.cn.202204020

High-efficiency IQ convolutional network structure for radio frequency fingerprint identification
DOI:
                        10.11887/j.cn.202204020
                    
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                        CUI Tianshu1,2CUI Tianshu
Key Laboratory of Electronics and Information Technology for Complex Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190;School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
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HUANG Yonghui1HUANG Yonghui
Key Laboratory of Electronics and Information Technology for Complex Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190
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SHEN Ming3SHEN Ming
Aalborg University, Aalborg DK-9220, Denmark
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ZHANG Ye1,2ZHANG Ye
Key Laboratory of Electronics and Information Technology for Complex Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190;School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
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CUI Kai1,2CUI Kai
Key Laboratory of Electronics and Information Technology for Complex Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190;School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
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ZHAO Wenjie1ZHAO Wenjie
Key Laboratory of Electronics and Information Technology for Complex Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190
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AN Junshe1,2AN Junshe
Key Laboratory of Electronics and Information Technology for Complex Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190;School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
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Affiliation:(1. Key Laboratory of Electronics and Information Technology for Complex Space Systems, National Space Science Center, Chinese Academy of Sciences, Beijing 100190;2. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China;3. Aalborg University, Aalborg DK-9220, Denmark)
Clc Number:TN92
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Abstract:

Existing convolutional neural networks, which are used for radio frequency fingerprints recognition, process time-sequenced IQ (in-phase and quadrature) signals as images directly, resulting in low recognition accuracy and high computation complexity. IQCNet(convolutional neural network structure based on IQ correlation features), an efficient convolutional network structure, was proposed. IQCNet firstly extracted IQ correlation features and time domain features, then obtained the average value of each channel features through adaptive average pooling, and finally used only one fully connected layer for classification. Experimental results under a variety of channel conditions show that IQCNet improves recognition accuracy greatly with lower computation complexity compared with traditional convolutional neural networks.

Key words:IQ signal; signal characteristics; radio frequency fingerprint; convolutional neural network; deep learning

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CUI Tianshu, HUANG Yonghui, SHEN Ming, ZHANG Ye, CUI Kai, ZHAO Wenjie, AN Junshe. High-efficiency IQ convolutional network structure for radio frequency fingerprint identification[J]. Journal of National University of Defense Technology,2022,44(4):180-189.

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Received:November 16,2020
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Online: July 20,2022
Published: August 28,2022

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