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

arXiv: https://arxiv.org/abs/2507.12133 · HTML: https://mlsanity.com/paper/2507.12133/

## 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.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers](https://mlsanity.com/paper/2005.05321/) (2020)
- [WiSig: A Large-Scale WiFi Signal Dataset for Receiver and Channel Agnostic RF Fingerprinting](https://mlsanity.com/paper/2112.15363/) (2021)
- [Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices](https://mlsanity.com/paper/2009.02267/) (2020)
- [A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authentication](https://mlsanity.com/paper/2108.04436/) (2021)
- [GAN-RXA: A Practical Scalable Solution to Receiver-Agnostic Transmitter Fingerprinting](https://mlsanity.com/paper/2303.14312/) (2023)
- [Mitigating Receiver Impact on Radio Frequency Fingerprint Identification via Domain Adaptation](https://mlsanity.com/paper/2404.08566/) (2024)
- [Edge AI-based Radio Frequency Fingerprinting for IoT Networks](https://mlsanity.com/paper/2412.10553/) (2024)
- [Time-frequency analysis of bivariate signals](https://mlsanity.com/paper/1609.02463/) (2016)
- [Radio Frequency Fingerprinting via Deep Learning: Challenges and Opportunities](https://mlsanity.com/paper/2310.16406/) (2023)
- [Improving RF-DNA Fingerprinting Performance in an Indoor Multipath Environment Using Semi-Supervised Learning](https://mlsanity.com/paper/2304.00648/) (2023)

## 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.