Universal Radio Frequency Fingerprinting across Receiving Systems Using Receiving Domain Separation

SUN Liting; LIU Zheng; HUANG Zhitao

doi:10.11999/JEIT240171

Volume 46 Issue 10

Oct. 2024

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SUN Liting, LIU Zheng, HUANG Zhitao. Universal Radio Frequency Fingerprinting across Receiving Systems Using Receiving Domain Separation[J]. Journal of Electronics & Information Technology, 2024, 46(10): 3966-3978. doi: 10.11999/JEIT240171

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SUN Liting, LIU Zheng, HUANG Zhitao. Universal Radio Frequency Fingerprinting across Receiving Systems Using Receiving Domain Separation[J]. Journal of Electronics & Information Technology, 2024, 46(10): 3966-3978. doi: 10.11999/JEIT240171

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Universal Radio Frequency Fingerprinting across Receiving Systems Using Receiving Domain Separation

doi: 10.11999/JEIT240171 cstr: 32379.14.JEIT240171

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

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

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

Available Online: 2024-07-22

Publish Date: 2024-10-30

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(27)

References

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