HyDRA: A Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916846261764096 |
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| author | Liu, Hanwen Huang, Yuhe Gong, Yifeng Zhai, Yanjie Lu, Jiaxuan |
| author_facet | Liu, Hanwen Huang, Yuhe Gong, Yifeng Zhai, Yanjie Lu, Jiaxuan |
| contents | Device recognition is vital for security in wireless communication systems, particularly for applications like access control. Radio Frequency Fingerprint Identification (RFFI) offers a non-cryptographic solution by exploiting hardware-induced signal distortions. This paper proposes HyDRA, a Hybrid Dual-mode RF Architecture that integrates an optimized Variational Mode Decomposition (VMD) with a novel architecture based on the fusion of Convolutional Neural Networks (CNNs), Transformers, and Mamba components, designed to support both closed-set and open-set classification tasks. The optimized VMD enhances preprocessing efficiency and classification accuracy by fixing center frequencies and using closed-form solutions. HyDRA employs the Transformer Dynamic Sequence Encoder (TDSE) for global dependency modeling and the Mamba Linear Flow Encoder (MLFE) for linear-complexity processing, adapting to varying conditions. Evaluation on public datasets demonstrates state-of-the-art (SOTA) accuracy in closed-set scenarios and robust performance in our proposed open-set classification method, effectively identifying unauthorized devices. Deployed on NVIDIA Jetson Xavier NX, HyDRA achieves millisecond-level inference speed with low power consumption, providing a practical solution for real-time wireless authentication in real-world environments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_12133 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HyDRA: A Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD Liu, Hanwen Huang, Yuhe Gong, Yifeng Zhai, Yanjie Lu, Jiaxuan Machine Learning Signal Processing Device recognition is vital for security in wireless communication systems, particularly for applications like access control. Radio Frequency Fingerprint Identification (RFFI) offers a non-cryptographic solution by exploiting hardware-induced signal distortions. This paper proposes HyDRA, a Hybrid Dual-mode RF Architecture that integrates an optimized Variational Mode Decomposition (VMD) with a novel architecture based on the fusion of Convolutional Neural Networks (CNNs), Transformers, and Mamba components, designed to support both closed-set and open-set classification tasks. The optimized VMD enhances preprocessing efficiency and classification accuracy by fixing center frequencies and using closed-form solutions. HyDRA employs the Transformer Dynamic Sequence Encoder (TDSE) for global dependency modeling and the Mamba Linear Flow Encoder (MLFE) for linear-complexity processing, adapting to varying conditions. Evaluation on public datasets demonstrates state-of-the-art (SOTA) accuracy in closed-set scenarios and robust performance in our proposed open-set classification method, effectively identifying unauthorized devices. Deployed on NVIDIA Jetson Xavier NX, HyDRA achieves millisecond-level inference speed with low power consumption, providing a practical solution for real-time wireless authentication in real-world environments. |
| title | HyDRA: A Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2507.12133 |