Radio Frequency Fingerprint Identification Based on Deep Complex Residual Network

Wang, Shenhua; Jiang, Hongliang; Xiaofang, Fang; Ying, Yulong; Li, Jingchao; Zhang, Bin

doi:10.1109/access.2020.3037206

Cited by 21 publications

(10 citation statements)

References 19 publications

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“…5. A detailed discussion of the complex convolution operator can be found in our previous work [30].…”

Section: Figure 4 the Framework Of The Proposed Methods Which Conducts Physical Layer Authentication For Iot Terminal Equipment Includingmentioning

confidence: 99%

Differential Complex-Valued Convolutional Neural Network-Based Individual Recognition of Communication Radiation Sources

Long

Zhang

2021

IEEE Access

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Data assaults from unauthorized access to the Internet of Things will induce severe intrusion and hazard to the whole network. Employing only traditional application layer password authentication approaches cannot guarantee the security of the communication system. Therefore, it is critical to develop a capable and efficient radio frequency fingerprints based physical layer authentication system. To incorporate the domain knowledge in more capable feature extracting and reduce information loss caused by converting RF baseband I/Q signals, we propose a novel differential complex-valued convolutional neural network based individual recognition approach of communication radiation sources in the paper. The proposed method can fully capture the nonlinearity of the RF baseband I/Q signals while decreasing the unfavorable impact of phase rotation induced by carrier frequency offset, which also significantly reduces the required data length of the collected steady-state data transmission section. The recognition performance evaluation on 20 wireless network card devices with duplicate batch, type, and manufacturer shows that the proposed approach has the best recognition performance compared with two conventional approaches whose recognition accuracies are lower than 95%, achieving the total recognition accuracy of 99.7%. Moreover, compared with constellation based approaches, which require at least 5,000 to 10,000 data points as input parameters, the proposed method can reduce the required data length of the collected steady-state data transmission section effectively, which is easier to implement in practical applications. INDEX TERMSRadio frequency fingerprint; constellation figure; physical layer authentication; differential process; complex-valued convolutional neural network This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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“…5. A detailed discussion of the complex convolution operator can be found in our previous work [30].…”

Section: Figure 4 the Framework Of The Proposed Methods Which Conducts Physical Layer Authentication For Iot Terminal Equipment Includingmentioning

confidence: 99%

Differential Complex-Valued Convolutional Neural Network-Based Individual Recognition of Communication Radiation Sources

Long

Zhang

2021

IEEE Access

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“…In order to illustrate the effectiveness of the method proposed in this paper, compare it with the radio frequency fingerprint extraction method of the contour stellar [28,29], and the deep convolutional neural network structure is unified as shown in Table 1, which is improved on the basis of AlexNet. Note: the x-axis is the In-phase signal and the y-axis is the Quadrature signal.…”

Section: B Application and Analysismentioning

confidence: 99%

Differential Contour Stellar-Based Radio Frequency Fingerprint Identification for Internet of Things

Ying

et al. 2021

IEEE Access

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Data attacks from illegal access devices of the Internet of Things will cause serious interference and threats to the entire network. It is difficult to ensure the security of the communication system only by relying on traditional application layer password authentication methods. Therefore, it is of great significance to design an effective physical layer authentication system based on radio frequency fingerprints. Regarding the issue above, this paper proposes a novel physical layer authentication method for Internet of Things based on differential contour stellar. Through the test of identification and authentication of 20 WiFi network card devices from same manufacturer, same type and same batch, the recognition accuracy rate can reach 98.6% by the proposed method. The proposed method can improve the effect of radio frequency fingerprint identification from three aspects: i. The differential processing can effectively reduce the negative influence of phase rotation caused by carrier frequency offset and Doppler effects; ii. The color processing can effectively reduce the negative influence of random noise caused by channel noise; iii. It is suitable for processing large-scale networks and the massive data they bring.

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“…Deep learning (DL) has also recently shown great promise in radiation source identification, with improved performance compared to traditional techniques. Xie Cunxiang et al [5] established a model integrating Hilbert-Huang transform and adversarial training in the research process, which can achieve good recognition results even when the training samples are small. Qu Lingzhi et al [6] proposed a communication radiation source identification method by embedding a double-layer attention mechanism in the residual network, which improved the recognition accuracy and stability.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised specific emitter identification of bispectrum features based on contrastive learning

Pengcheng,

Lei,

Haitao

et al. 2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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Aiming at the problem that features of communication emitter data without label information are different to extract and the classification acquisition is not high, this paper cites the contrastive learning theory, constructs a residual network with two parameters sharing as the backbone network, conducts contrastive learning on the rectangular integral bispectral features of signal augmented samples, and further extracts the feature presentation with more differentiation. To this end, the feature separability between samples from different emitters is enhanced. Then, the new features extracted are used for contrastive learning at the cluster level to complete the tasks of classification and identification. Compared with other unsupervised learning algorithms, the proposed method achieves a better identification accuracy of about 78% through experiences on the data set of measured ultra-short wave communication stations.

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Radio Frequency Fingerprint Identification Based on Deep Complex Residual Network

Cited by 21 publications

References 19 publications

Differential Complex-Valued Convolutional Neural Network-Based Individual Recognition of Communication Radiation Sources

Differential Complex-Valued Convolutional Neural Network-Based Individual Recognition of Communication Radiation Sources

Differential Contour Stellar-Based Radio Frequency Fingerprint Identification for Internet of Things

Unsupervised specific emitter identification of bispectrum features based on contrastive learning

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