A radio frequency fingerprinting method for wireless node identity authentication

By converting wireless signals into time-spectrum graphs and training them using feature projection and alignment-free feature distillation loss from teacher and student identification models, the feature extraction challenge for wireless node authentication in short-frame wireless signal scenarios is solved, achieving efficient authentication and lightweight deployment.

CN122262833APending Publication Date: 2026-06-23CHANGCHUN UNIV OF SCI & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHANGCHUN UNIV OF SCI & TECH
Filing Date
2026-05-26
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

Existing radio frequency fingerprinting methods struggle to effectively extract the identity features of wireless nodes in short-frame wireless signal scenarios, and traditional distillation methods fail to establish effective supervisory relationships between heterogeneous models, resulting in difficulties in deploying recognition models and a high false positive rate.

Method used

By converting the training wireless signal samples into time-spectrum samples, global and local features are extracted using teacher and student identification models. Feature associations are established through cross-dimensional correlation matrices and alignment-free attention weights, and alignment-free feature distillation loss is used for training to obtain a lightweight RF fingerprint recognition model.

Benefits of technology

It enhances the distinguishability of wireless node identity features, reduces the misjudgment rate in short-frame wireless signal scenarios, improves the consistency of identity category determination, and maintains the model's lightweight deployment advantage.

✦ Generated by Eureka AI based on patent content.

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Abstract

The present application relates to the technical field of wireless communication security, in particular to a radio frequency fingerprint identification method for wireless node identity authentication. The method solves the problems of weak radio frequency fingerprint characteristics in short frame wireless signals, insufficient discrimination ability of lightweight models, and difficulty in rigid alignment and distillation of heterogeneous teacher-student models. The method obtains training wireless signal samples with identity labels and converts them into time-frequency spectrum samples; inputs the time-frequency spectrum samples into a teacher recognition model and a student recognition model to obtain classification results and global features; projects the teacher global features and the student global features, generates a cross-dimensional correlation matrix according to the projection feature component correlation, and normalizes to obtain alignment-free attention weights; constructs an alignment-free feature distillation loss based on the weights, and jointly trains to obtain a lightweight radio frequency fingerprint identification model; the model is used to output the identity category of the wireless node to be verified during inference, which is used for wireless node access authentication and physical layer identity authentication.
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