A Radio Frequency Fingerprint Open-set Identification MethodCombining Multi-scale Wavelet Front-end and Hyperspherical Metric Learning

TIAN Xinyu; LI Zirui; ZHENG Qinghe; ZHOU Fuhui; YU Lisu; HUANG Chongwen; JIANG Weiwei; SHU Feng; ZHAO Yizhe

doi:10.11999/JEIT260214

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TIAN Xinyu, LI Zirui, ZHENG Qinghe, ZHOU Fuhui, YU Lisu, HUANG Chongwen, JIANG Weiwei, SHU Feng, ZHAO Yizhe. A Radio Frequency Fingerprint Open-set Identification MethodCombining Multi-scale Wavelet Front-end and Hyperspherical Metric Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260214

Citation:

TIAN Xinyu, LI Zirui, ZHENG Qinghe, ZHOU Fuhui, YU Lisu, HUANG Chongwen, JIANG Weiwei, SHU Feng, ZHAO Yizhe. A Radio Frequency Fingerprint Open-set Identification MethodCombining Multi-scale Wavelet Front-end and Hyperspherical Metric Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260214

Citation:

TIAN Xinyu, LI Zirui, ZHENG Qinghe, ZHOU Fuhui, YU Lisu, HUANG Chongwen, JIANG Weiwei, SHU Feng, ZHAO Yizhe. A Radio Frequency Fingerprint Open-set Identification MethodCombining Multi-scale Wavelet Front-end and Hyperspherical Metric Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260214

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A Radio Frequency Fingerprint Open-set Identification MethodCombining Multi-scale Wavelet Front-end and Hyperspherical Metric Learning

doi: 10.11999/JEIT260214 cstr: 32379.14.JEIT260214

1.
School of Intelligent Engineering, Shandong Management University, Jinan 250357, China
2.
School of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
3.
School of Information Engineering, Nanchang University, Nanchang 330031, China
4.
School of Information and Electronic Engineering, Zhejiang University, Hangzhou 310058, China
5.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
6.
School of Information and Communication Engineering, Hainan University, Haikou 570228, China
7.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The National Natural Science Foundation of China (62401070), Shandong Provincial Natural Science Foundation (ZR2019ZD01, ZR2023QF125), Shandong Provincial Youth Innovation Team Plan of Higher Education Institutions (2024KJH005), Shandong Provincial Science and Technology Based Small and Medium sized Enterprises Innovation Capability Enhancement Project (2024TSGC0055)

Received Date: 2026-02-28
Accepted Date: 2026-03-27
Rev Recd Date: 2026-03-27

Available Online: 2026-04-22

Abstract

Abstract

Objective Open-set Radio Frequency Fingerprint (RFF) identification under low Signal-to-Noise Ratio (SNR) conditions is challenging because fingerprint features are easily masked by noise, multipath effects induce nonlinear distortions, and existing methods struggle with feature extraction and unknown device detection. This study proposes a deep learning framework that integrates a multi-scale wavelet front-end with hyperspherical metric learning to achieve robust open-set RFF identification. Methods The proposed method, MS-RANet, comprises three key components. First, a multi-scale wavelet front-end based on one-dimensional stationary wavelet transform performs full-resolution, multi-scale decomposition of I/Q signals, preserving discriminative fingerprint information while suppressing noise. Second, a multi-scale residual attention network incorporates deep residual learning, global self-attention, and Bidirectional LSTM (BiLSTM) to enhance sensitivity to subtle fingerprint features and capture long-range temporal dependencies. Third, hyperspherical metric learning constrains the feature space onto a unit hypersphere, optimizing angular margins to produce compact intra-class and separable inter-class feature distributions. Unknown devices are subsequently detected using cosine similarity. Results and Discussions Experiments on a high-fidelity IEEE 802.11 simulation dataset demonstrate the effectiveness of MS-RANet. The method achieves an average classification accuracy of 65.34% across SNR levels from –5 dB to 20 dB, and an Area Under the Curve (AUC) of 0.81 at –5 dB SNR, outperforming DNN, GRU, CNN-LSTM, ResNet50, and DRSN-CA. Confusion matrices and Receiver Operating Characteristic (ROC) curves confirm robustness under extreme channel conditions. t-SNE visualization shows well-separated, compact clusters for known devices, while unknown samples are effectively isolated from known class regions. Ablation studies verify the contributions of the multi-scale wavelet front-end, global attention, BiLSTM, and hyperspherical metric learning modules. Conclusions This study presents a robust open-set RFF identification method combining a multi-scale wavelet front-end with hyperspherical metric learning. The framework exhibits strong noise resilience, enhanced feature discrimination, and reliable detection of unknown devices under low-SNR and multipath fading conditions. Future work will focus on reducing computational complexity, improving inference speed, evaluating generalization across diverse scenarios and protocols, and integrating the method with complementary physical-layer security mechanisms for collaborative authentication.
- Radio frequency fingerprint identification,
- Multi-scale wavelet,
- Hyperspherical metric learning,
- Multi scale residual attention

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References(25)

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