Universal Radio Frequency Fingerprinting across Receiving Systems UsingReceiving Domain Separation

SUN Liting; LIU Zheng; HUANG Zhitao

doi:10.11999/JEIT240171

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SUN Liting, LIU Zheng, HUANG Zhitao. Universal Radio Frequency Fingerprinting across Receiving Systems UsingReceiving Domain Separation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240171

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SUN Liting, LIU Zheng, HUANG Zhitao. Universal Radio Frequency Fingerprinting across Receiving Systems UsingReceiving Domain Separation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240171

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Universal Radio Frequency Fingerprinting across Receiving Systems UsingReceiving Domain Separation

doi: 10.11999/JEIT240171

College of Electronic Science and Technology, National University of Defense Technology Changsha 410073, China

Funds: The National Natural Science Foundation of China (62301575), Youth Independent Innovation Science Fund Project of National University of Defense Technology (ZK2023-19)

Received Date: 2024-03-14
Rev Recd Date: 2024-07-15

Available Online: 2024-07-22

Abstract

Abstract

Due to the coupling effect of emitter distortion and receiver distortion, the actual received signal contains the information of the current emitter system and the receiving system, which makes the Radio Frequency Fingerprinting (RFF) technology unable to be generalized in cross-receiving system scenarios. In order to eliminate the effect of receiver, in this paper, a universal RFF method across receiving systems based on receiving domain separation is proposed which considers the influence of the receiver as a separate scope. Through the dual-label multi-channel fusion feature and domain separation adversarial reconstruction method, after trained with multi-receiver data in the source domain, the proposed method can separate domains of transmitting and receiving, extract emitter fingerprint information, which improves the generalization of RFF in scenarios such as cross-receiving system and cross-platform. Compared with the existing cross-receiver RFF methods, the proposed method can truly adapt to the actual unsupervised scenario. And the more the number of source domain receivers participating in the training, the better the domain adaptation effect. It can be directly applied to the new receiving system without repeated training, which has high practical application value.

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References(28)

References

[1]	XU Zhengwei, HAN Guangjie, LI Liu, et al. A lightweight specific emitter identification model for IIoT devices based on adaptive broad learning[J]. IEEE Transactions on Industrial Informatics, 2023, 19(5): 7066–7075. doi: 10.1109/TII.2022.3206309.
[2]	ZHA Haoran, WANG Hanhong, FENG Zhongming, et al. LT-SEI: Long-tailed specific emitter identification based on decoupled representation learning in low-resource scenarios[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 52(1): 929–943. doi: 10.1109/TITS.2023.3308716.
[3]	韦建宇, 俞璐. 通信辐射源个体识别中的特征提取方法综述[J]. 通信技术, 2022, 55(6): 681–687. doi: 10.3969/j.issn.1002-0802.2022.06.001. WEI Jianyu and YU Lu. Overview of radio frequency fingerprint extraction in communication specific emitter identification[J]. Communications Technology, 2022, 55(6): 681–687. doi: 10.3969/j.issn.1002-0802.2022.06.001.
[4]	HE Boxiang and WANG Fanggang. Specific emitter identification via sparse Bayesian learning versus model-agnostic meta-learning[J]. IEEE Transactions on Information Forensics and Security, 2023, 18: 3677–3691. doi: 10.1109/TIFS.2023.3287073.
[5]	REHMAN S U, SOWERBY K W, and COGHILL C. Analysis of impersonation attacks on systems using RF fingerprinting and low-end receivers[J]. Journal of Computer and System Sciences, 2014, 80(3): 591–601. doi: 10.1016/j.jcss.2013.06.013.
[6]	RAMSEY B W, STUBBS T D, MULLINS B E, et al. Wireless infrastructure protection using low-cost radio frequency fingerprinting receivers[J]. International Journal of Critical Infrastructure Protection, 2015, 8: 27–39. doi: 10.1016/j.ijcip.2014.11.002.
[7]	乐波, 王桂良, 黄渊凌, 等. 接收机畸变对辐射源指纹识别的影响[J]. 电讯技术, 2020, 60(3): 273–278. doi: 10.3969/j.issn.1001-893x.2020.03.005. LE Bo, WANG Guiliang, HUANG Yuanling, et al. Influence of receiver distortion characteristics on specific emitter identification[J]. Telecommunication Engineering, 2020, 60(3): 273–278. doi: 10.3969/j.issn.1001-893x.2020.03.005.
[8]	. ZHENG Yenan, YING Wenwei, HONG Shaohua, et al. A method for cross-receiver specific emitter identification based on CBAM-CNN-BDA[C]. 2022 IEEE 4th International Conference on Civil Aviation Safety and Information Technology (ICCASIT), Dali, China, 2022: 1320–1324. doi: 10.1109/ICCASIT55263.2022.9987240.
[9]	FANG Yuyuan, WEI Song, ZHAO Yang, et al. Radar-specific emitter identification with only envelope power based on multidimensional complex noncentral chi-square classifier[J]. IEEE Sensors Journal, 2023, 23(17): 20223–20235. doi: 10.1109/JSEN.2023.3298352.
[10]	TAN Kaiwen, YAN Wenjun, ZHANG Limin, et al. Semi-supervised specific emitter identification based on bispectrum feature extraction CGAN in multiple communication scenarios[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(1): 292–310. doi: 10.1109/TAES.2022.3184619.
[11]	ZHU Chenyu, LIU Liang, and PENG Xiaoyan. Specific emitter identification based on temporal convolutional network sequence processing[J]. IEEE Communications Letters, 2023, 27(10): 2667–2671. doi: 10.1109/LCOMM.2023.3312390.
[12]	WU Zitao, WANG Fanggang, and HE Boxiang. Specific emitter identification via contrastive learning[J]. IEEE Communications Letters, 2023, 27(4): 1160–1164. doi: 10.1109/LCOMM.2023.3247900.
[13]	. BALDINI G, GIULIANI R, GENTILE C, et al. Measures to address the lack of portability of the RF fingerprints for radiometric identification[C]. 9th IFIP International Conference on New Technologies, Mobility and Security, Paris, France, 2018: 1–5. doi: 10.1109/NTMS.2018.8328703.
[14]	SHI Mengkai, HUANG Yuanling, and WANG Guiliang. Carrier leakage estimation method for cross-receiver specific emitter identification[J]. IEEE Access, 2021, 9: 26301–26312. doi: 10.1109/ACCESS.2021.3058167.
[15]	HE Boxiang and WANG Fanggang. Cooperative specific emitter identification via multiple distorted receivers[J]. IEEE Transactions on Information Forensics and Security, 2020, 15: 3791–3806. doi: 10.1109/TIFS.2020.30017210.
[16]	. FAWAZ H I, DEL GROSSO G, KERDONCUFF T, et al. Deep unsupervised domain adaptation for time series classification: A benchmark[EB/OL]. https://arXiv.org/abs/2312.09857v2, 2023.
[17]	. LI Ya, GONG Mingming, TIAN Xinmei, et al. Domain generalization via conditional invariant representations[C]. Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, USA, 2018: 3579–3587. doi: 10.1609/aaai.v32i1.11682.
[18]	. MATSUURA T and HARADA T. Domain generalization using a mixture of multiple latent domains[C]. Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, USA, 2020: 11749–11756. doi: 10.1609/aaai.v34i07.6846.
[19]	GANIN Y, USTINOVA E, AJAKAN H, et al. Domain-adversarial training of neural networks[J]. The Journal of Machine Learning Research, 2016, 17(1): 2096–2030.
[20]	BOUSMALIS K, TRIGEORGIS G, SILBERMAN N, et al. Domain separation networks[C]. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, 2016: 343–351.
[21]	SUN Liting, WANG Xiang, HUANG Zhitao, et al. Radio-frequency fingerprint extraction based on feature inhomogeneity[J]. IEEE Internet of Things Journal, 2022, 9(18): 17292–17308. doi: 10.1109/JIOT.2022.3154595.
[22]	. SHI Mengkai, HUANG Yuanling, and WANG Guiliang. Carrier leakage estimation method for cross-receiver specific emitter identification[J]. IEEE Access, 2021, 9: 26301–26312. doi: 10.1109/ACCESS.2021.3058167. (查阅网上资料,本条文献与第14条重复,请确认) .
[23]	. SRIDHARAN G. Phase noise in multi-carrier systems[D]. [Master dissertation], University of Toronto, 2010.
[24]	HUANG Yuanling and ZHENG Hui. Theoretical performance analysis of radio frequency fingerprinting under receiver distortions[J]. Wireless Communications and Mobile Computing, 2015, 15(5): 823–833. doi: 10.1002/wcm.2386.
[25]	陈翔, 汪连栋, 许雄, 等. 基于Raw I/Q和深度学习的射频指纹识别方法综述[J]. 雷达学报, 2023, 12(1): 214–234. doi: 10.12000/JR22140. CHEN Xiang, WANG Liandong, XU Xiong, et al. A review of radio frequency fingerprinting methods based on Raw I/Q and deep learning[J]. Journal of Radars, 2023, 12(1): 214–234. doi: 10.12000/JR22140.
[26]	. 郭瑞鹏. 基于深度学习的雷达辐射源个体识别技术研究[D]. [硕士论文], 西安电子科技大学, 2022. doi: 10.27389/d.cnki.gxadu.2022.002199. GUO Ruipeng. Research on individual identification technology of radar emitter based on deep learning[D]. [Master dissertation], Xidian University, 2022. doi: 10.27389/d.cnki.gxadu.2022.002199.
[27]	DING Lida, WANG Shilian, WANG Fanggang, et al. Specific emitter identification via convolutional neural networks[J]. IEEE Communications Letters, 2018, 22(12): 2591–2594. doi: 10.1109/LCOMM.2018.2871465.
[28]	. AL-SHAWABKA A, RESTUCCIA F, D’ORO S, et al. Exposing the fingerprint: Dissecting the impact of the wireless channel on radio fingerprinting[C]. IEEE Conference on Computer Communications, Toronto, Canada, 2020: 646–655. doi: 10.1109/INFOCOM41043.2020.9155259.