标题： HyDRA：一种混合双模式网络，用于封闭集和开放集的RFFI，具有优化的VMD 显示英文标题

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

摘要： 设备识别对于无线通信系统的安全性至关重要，特别是在访问控制等应用中。射频指纹识别（RFFI）通过利用硬件引起的信号失真提供了一种非加密解决方案。本文提出了HyDRA，一种混合双模式射频架构，该架构结合了优化的变分模态分解（VMD）与基于卷积神经网络（CNN）、Transformer和Mamba组件融合的新架构，旨在支持封闭集和开放集分类任务。优化的VMD通过固定中心频率并使用闭式解来提高预处理效率和分类准确性。HyDRA采用Transformer动态序列编码器（TDSE）进行全局依赖建模，以及Mamba线性流编码器（MLFE）进行线性复杂度处理，以适应不同的条件。在公共数据集上的评估表明，在封闭集场景中达到了最先进的（SOTA）准确率，并在我们提出的开放集分类方法中表现出稳健的性能，能够有效识别未经授权的设备。部署在NVIDIA Jetson Xavier NX上，HyDRA实现了毫秒级的推理速度并具有低功耗，为现实环境中的实时无线认证提供了实用的解决方案。 显示英文摘要

摘要： 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.