XU Q , ZHENG R , SAAD W , et al. Device fingerprinting in wireless networks:challenges and opportunities [J ] . IEEE Communications Surveys & Tutorials , 2016 , 18 ( 1 ): 94 - 104 .

WANG S , PENG L N , FU H , et al. Aconvolutional neural network-based RF fingerprinting identification scheme for mobile phones [C ] // Proceedings of IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops.Toronto, ON, Canada: IEEE Press , 2020 : 15 - 120 .

PENG L N , ZHANG J Q , LIU M , et al. Deep learning based RF fingerprint identification using differential constellation trace figure [J ] . IEEE Transactions on Vehicular Technology , 2019 , 69 ( 1 ): 1091 - 1095 . DOI: 10.1109/TVT.25 http://doi.org/10.1109/TVT.25 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=25 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=25

TAMURA H , ANAGISAWA K , SHIRANE A , et al. Wireless devices identification with light-weight convolutional neural network operating on quadrant IQ transition image [C]//Proceedings of the 2020 18th IEEE International New Circuits and Systems Conference . Montreal, QC, Canada: IEEE Press , 2020 : 106 - 109 .

RU X H , LIU Z , HUANG Z T , et al. Evaluation of unintentional modulation for pulse compress-ion signals based on spectrum asymmetry [J ] . IET Radar, Sonar & Navigation , 2017 , 11 ( 4 ): 656 - 663 . DOI: 10.1049/rsn2.v11.4 http://doi.org/10.1049/rsn2.v11.4 https://onlinelibrary.wiley.com/toc/17518792/11/4 https://onlinelibrary.wiley.com/toc/17518792/11/4

WANG W , LIU H , YANG J A , et al. Specific emitter identification using decomposed hierarchical feature extraction methods [C ] // Proceedings of International Conference on Natural Computation.Piscataway, NJ, US: IEEE Press , 2017 : 1639 - 1643 .

WONG L J , HEADLEY W C , MICHAELS A J . Specific emitter identification using convolutional neural network-based IQ imbalance estimators [J ] . IEEE Access , 2019 , 7 : 33544 - 33555 . DOI: 10.1109/Access.6287639 http://doi.org/10.1109/Access.6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

RAM M , BANER C , ROY D , et al. Exploitingspatio-temporal properties of I/Q signal data using 3D convolution for RF transmitter ientification [J ] . IEEE Journal of Radio Frequency Identification , 2021 , 99 : 113 - 127 .

LING Y , JIA J C , WANG S , et al. Wireless device identification based on radio frequency fingerprint features[C]//Proceedings of the 2020 IEEE International Conference on Communications . Dublin, Ireland:IEEE , 2020 : 1 - 6 .

COGHILL C , REHMAN S U , SOWERBY K W . Radio-frequency fingerprinting for mitigating primary user emulation attack in low-end cognitive radios [J ] . IET Communications , 2014 , 8 ( 8 ): 1274 - 128 DOI: 10.1049/cmu2.v8.8 http://doi.org/10.1049/cmu2.v8.8 https://onlinelibrary.wiley.com/toc/17518636/8/8 https://onlinelibrary.wiley.com/toc/17518636/8/8

DING L D , WANG S L , WANG F G , et al. Specific emitter identification via convolutional neural networks [J ] . IEEE Communications Letters , 2018 , 22 ( 12 ): 2591 - 2594 . DOI: 10.1109/LCOMM.2018.2871465 http://doi.org/10.1109/LCOMM.2018.2871465 https://ieeexplore.ieee.org/document/8469100/ https://ieeexplore.ieee.org/document/8469100/

YAO Y Y , YU L , CHEN Y M . Specific emitter identification based on square integral bispectrum features[C]//Proceedings of the 2020 IEEE 20th International Conference on Communication Technology . Nanning, China:IEEE , 2020 : 1311 - 1314 .

GONG J L , XU X D , LEI Y K . Unsupervised specific emitter identification method using radio-frequency fingerprint embedded infoGAN [J ] . IEEE Transactions on Information Forensics and Security , 2020 , 15 : 2898 - 2913 . DOI: 10.1109/TIFS.10206 http://doi.org/10.1109/TIFS.10206 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=10206 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=10206

CHEN S L , XIE F Y , CHEN Y , et al. Identification of wireless transceiver devices using radio frequency (RF) fingerprinting based on STFT analysis to enhance authentication security[C]//Proceedings of the 2017 IEEE 5th International Symposium on Electromagnetic Compatibility . Beijing, China: IEEE , 2017 : 1 - 5 .

TU Y , ZHANG Z , LI Y B , et al. Research on the Internet of Things device recognition based on RF-fingerprinting [J ] . IEEE Access , 2019 , 7 : 37426 - 37431 . DOI: 10.1109/Access.6287639 http://doi.org/10.1109/Access.6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

ZHOU X Y , HU A Q , LI G Y , et al. A robust radio-frequency fingerprint extraction scheme for practical device recognition [J ] . IEEE Internet of Things Journal , 2021 , 99 : 1276 - 11289 .

Wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (WPANs):IEEE 802.15.4 [S ] . New York, NY, US:IEEE , 2006 .

MOSTAFA A E , WONG V W , ZHOU Y , et al. Aggregate preamble sequence design and detection for massive IoT with deep learning [J ] . IEEE Transactions on Vehicular Technology , 2021 , 70 ( 4 ): 3800 - 3816 . DOI: 10.1109/TVT.2021.3064868 http://doi.org/10.1109/TVT.2021.3064868 https://ieeexplore.ieee.org/document/9374107/ https://ieeexplore.ieee.org/document/9374107/

RAMSEY B W , TEMPLE M A , MULLINS B E . PHY foundation for multi-factor ZigBee node authentication[C]//Proceedings of the 2012 IEEE Global Communications Conference . Anaheim, CA, US: IEEE Press , 2012 : 795 - 800 .

Al-SHAWABKA A , RESTUCCIA F , D’ORO S , et al. Massive-scale I/Q datasets for WiFi radio fingerprinting [J ] . Computer Networks , 2020 , 182 : 107566 . DOI: 10.1016/j.comnet.2020.107566 http://doi.org/10.1016/j.comnet.2020.107566 https://linkinghub.elsevier.com/retrieve/pii/S1389128620312123 https://linkinghub.elsevier.com/retrieve/pii/S1389128620312123

MITRA S K . Digital signal processing:a computer-based approach [M ] . 3rd ed. Tsinghua University Press , 2012 .

PARHI K , AYINAL M . Low-complexity welch power spectral density computation [J ] . IEEE Transactions on Circuits and Systems I:Regular Papers , 2014 , 61 ( 1 ): 172 - 182 . DOI: 10.1109/TCSI.2013.2264711 http://doi.org/10.1109/TCSI.2013.2264711 http://ieeexplore.ieee.org/document/6525419/ http://ieeexplore.ieee.org/document/6525419/

ZHANG G C , DU R X , SHI T L . Extracting gear fault features using integrated bispectrum[C]//Robotics Intelligent Systems and Signal Processing . Changsha, China: IEEE , 2003 : 548 - 553 .

WEI D X , ZHANG S N , CHEN S , et al. Research on anti-jamming technology of chaotic composite shortrange detection system based on underdetermined signal separation and spectral analysis [J ] . IEEE Access , 2019 , 7 : 42298 - 42308 . DOI: 10.1109/Access.6287639 http://doi.org/10.1109/Access.6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

FAILLI M . Digital land mobile radio communications:COST 207 [R ] . London: Commission of the European Communities , 1989 .

FADUL M , REISING D , LOVELESS T , et al. RF-DNA fingerprint classification of OFDM signals using a rayleigh fading channel model[C]// Proceedings of the 2019 IEEE Wireless Communications and Networking Conference . Marrakesh, Morocco: IEEE Press , 2019 : 1 - 7 .

李晨明 . 非合作通信中的盲均衡技术研究 [D ] . 西安 : 西安电子科技大学 , 2020 : 111 .

LI C . Research on Equalization Technology in Non-cooperative Communication [D ] . Xi’an : Xidian University , 2020 : 111 . (in Chinese)

Al-SHAWABKA A , RESTUCCIA F , D’ORO S , et al. Exposing the fingerprint: dissecting the impact of the wireless channel on radio fingerprinting[C]//Proceedings of the IEEE INFOCOM 2020-IEEE Conference on Computer Communications . Toronto, Canada: IEEE , 2020 : 646 - 655 .

石荣 , 吴聪 . 信噪比的不同定义及对调制识别性能评估的影响 [J ] . 通信技术 , 2019 , 52 ( 7 ): 1556 - 1562 .

SHI R , WU C . Different definitions of SNR and its influence on performance evaluation of modulation recognition [J ] . Communications Technology , 2019 , 52 ( 7 ): 1556 - 1562 . (in Chinese)

PAN Y W , YANG S H , PENG H , et al. Specific emitter identification based on deep residual networks [J ] . IEEE Access , 2019 , 7 : 54425 - 54434 . DOI: 10.1109/ACCESS.2019.2913759 http://doi.org/10.1109/ACCESS.2019.2913759 Specific emitter identification (SEI) enables the discrimination of individual radio emitters with the external features carried by the received waveforms. This identification technique has been widely adopted in military and civil applications. However, many previous methods based on hand-crafted features are subject to the present expertise. To remedy these shortcomings, this paper presents a novel SEI algorithm using deep learning architecture. First, we perform Hilbert-Huang transform on the received signal and convert the resulting Hilbert spectrum into a grayscale image. As a signal representation, the Hilbert spectrum image has high information integrity and can provide abundant information about the nonlinear and non-stationary characteristics of signals for identifying emitters. Thereafter, we construct a deep residual network for learning the visual differences refiected in the Hilbert spectrum images. By using the residual architectures, we effectively address the degradation problem, which improves efficiency and generalization. From our analysis, the proposed approach combines high information integrity with low complexity, which outperforms previous studies in the literature. The simulation results validate that the Hilbert spectrum image is a successful signal representation, and also demonstrate that the fingerprints extracted from raw images using deep learning are more effective and robust than the expert ones. Furthermore, our method has the capability of adapting to signals collected under various conditions.

陈悦 , 雷迎科 , 李昕 , 等 . 基于IQ图特征的通信辐射源个体识别 [J ] . 信号处理 , 2021 , 37 ( 1 ): 120 - 125 .

CHEN Y , LEI Y K , LI X , et al. Specific emitter identification of communication radiation source based on the characteristics IQ graph features [J ] . Journal of Signal Processing , 2021 , 37 ( 1 ): 120 - 125 . (in Chinese)

梁华东 , 韩江洪 . 采用双谱多类小波包特征的雷达信号聚类分选 [J ] . 光子学报 , 2014 , 43 ( 3 ): 158 - 165 .

LIANG H D , HAN J H . Clustering and sorting radar signals based on multi wavelet packets characteristics of bispectrum [J ] . Acta Photonica Sinica , 2014 , 43 ( 3 ): 158 - 165 . (in Chinese)

ZHAO W Z , DU S H . Spectral-spatial feature extraction for hyperspectral image classifi-cation: a dimension reduction and deep learning approach [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2016 , 54 ( 8 ): 4544 - 4554 . DOI: 10.1109/TGRS.2016.2543748 http://doi.org/10.1109/TGRS.2016.2543748 http://ieeexplore.ieee.org/document/7450160/ http://ieeexplore.ieee.org/document/7450160/