mlsanityData to 14 Oct 2026

HyDRA: A Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD

arXiv 2507.12133 ↗First posted 16 Jul 2025cs.LG eess.SPcited by 0

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Abstract (arXiv)

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. The source code is published on https://github.com/Crazy-Bull/HyDRA.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 52 references that are in the map. First posted on arXiv 16 Jul 2025.

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References · 13

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  3. 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 345
  4. 2020Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifierscited by 28
  5. 2021WiSig: A Large-Scale WiFi Signal Dataset for Receiver and Channel Agnostic RF Fingerprintingcited by 21
  6. 2020Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devicescited by 10
  7. 2021A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authenticationcited by 7
  8. 2023GAN-RXA: A Practical Scalable Solution to Receiver-Agnostic Transmitter Fingerprintingcited by 5
  9. 2024Mitigating Receiver Impact on Radio Frequency Fingerprint Identification via Domain Adaptationcited by 3
  10. 2024Edge AI-based Radio Frequency Fingerprinting for IoT Networkscited by 3
  11. 2016Time-frequency analysis of bivariate signalscited by 2
  12. 2023Radio Frequency Fingerprinting via Deep Learning: Challenges and Opportunitiescited by 2
1 more
  1. 2023Improving RF-DNA Fingerprinting Performance in an Indoor Multipath Environment Using Semi-Supervised Learningcited by 1

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  2. 2025An Adversarial-Driven Experimental Study on Deep Learning for RF Fingerprinting
  3. 2025Noise-Robust Radio Frequency Fingerprint Identification Using Denoise Diffusion Model
  4. 2025Protocol-agnostic and Data-free Backdoor Attacks on Pre-trained Models in RF Fingerprinting
  5. 2024A Survey of Machine Learning-based Physical-Layer Authentication in Wireless Communications
  6. 2024Augmenting Training Data with Vector-Quantized Variational Autoencoder for Classifying RF Signals
  7. 2026APEG: Adaptive Physical Layer Authentication with Channel Extrapolation and Generative AI
  8. 2025Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach