Radio Frequency Fingerprint Identification With Hybrid Time-Varying Distortions

被引:18
|
作者
He, Jiashuo [1 ]
Huang, Sai [1 ]
Chang, Shuo [1 ]
Wang, Fanggang [2 ]
Shen, Ba-Zhong
Feng, Zhiyong [1 ]
机构
[1] Beijing Univ Posts & Telecommun, Key Lab Univ Wireless Commun, Minist Educ, Beijing 100876, Peoples R China
[2] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China
基金
中国国家自然科学基金;
关键词
Feature extraction; hybrid time-varying distortions; radio frequency fingerprint identification; Rayleigh fading channel; signal preprocessing; OFDM SYSTEMS; EMITTER IDENTIFICATION; CHANNEL ESTIMATION; OFFSET; TECHNOLOGIES; INTERNET; THINGS;
D O I
10.1109/TWC.2023.3245070
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Radio frequency fingerprint identification (RFFI) is a promising physical layer security technique that employs the hardware-introduced features extracted from the received signals for device identification. In this paper, we consider an RFFI problem in the presence of hybrid time-varying distortions (HTVDs) induced by multipath fading channel, carrier frequency offset (CFO), and phase offset. To solve this problem, an HTVDs-robust RFFI framework is proposed. Firstly, we derive that the residual HTVDs after CFO correction can be approximated as multiplicative interference in the frequency domain. Secondly, we define a novel signal analysis dimension named spectral quotient (SQ) representation and then present the spectral circular shift division (SCSD) method to generate the HTVDs-robust SQ signals, where the multiplicative interference can be suppressed. Thereafter, the statistics including root mean square (RMS), variance (VAR), skewness (SKE), and kurtosis (KUR) are extracted from the real and imaginary components of the SQ signals, respectively. Finally, the statistical features are used for the training and testing of the support vector machine (SVM) classifiers. To further enhance the performance of the proposed RFFI scheme, we also present the spectral circular multi-shift division (SCMSD) method, which increases the flexibility in the generation of the HTVDs-robust SQ signals. Given what we knew, this is the first time attempting to mitigate the HTVDs by leveraging the strong frequency correlation at the neighboring subcarriers in the multivariate hypothesis tasks. Compared to several handcraft feature-based RFFI methods, the proposed method exhibits superior identification accuracy and strong robustness. Experimental results show that the proposed RFFI scheme can achieve the accuracy of 91.3% with five devices and 86.4% with sixteen devices when the classifiers are trained with the additive white Gaussian noise but are tested with the Rayleigh channel.
引用
收藏
页码:6724 / 6736
页数:13
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