Factorized Disentangled Representation Learning for Interpretable Radio Frequency Fingerprint

In response to the rapid growth of Internet of Things (IoT) devices and rising security risks, Radio Frequency Fingerprint (RFF) has become key for device identification and authentication. However, various changing factors - beyond the RFF itself - can be entangled from signal transmission to reception, reducing the effectiveness of RFF Identification (RFFI). Existing RFFI methods mainly rely on domain adaptation techniques, which often lack explicit factor representations, resulting in less robustness and limited controllability for downstream tasks. To tackle this problem, we propose a novel Disentangled Representation Learning (DRL) framework that learns explicit and independent representations of multiple factors, including the RFF. Our framework introduces modules for disentanglement, guided by the principles of explicitness, modularity, and compactness. We design two dedicated modules for factor classification and signal reconstruction, each with tailored loss functions that encourage effective disentanglement and enhance support for downstream tasks. Thus, the framework can extract a set of interpretable vectors that explicitly represent corresponding factors. We evaluate our approach on two public benchmark datasets and a self-collected dataset. Our method achieves impressive performance on multiple DRL metrics. We also analyze the effectiveness of our method on downstream RFFI task and conditional signal generation task. All modules of the framework contribute to improved classification accuracy, and enable precise control over conditional generated signals. These results highlight the potential of our DRL framework for interpretable and explicit RFFs.

MAMCA -- Optimal on Accuracy and Efficiency for Automatic Modulation Classification with Extended Signal Length

With the rapid growth of the Internet of Things ecosystem, Automatic Modulation Classification (AMC) has become increasingly paramount. However, extended signal lengths offer a bounty of information, yet impede the model's adaptability, introduce more noise interference, extend the training and inference time, and increase storage overhead. To bridge the gap between these requisites, we propose a novel AMC framework, designated as the Mamba-based Automatic Modulation ClassificAtion (MAMCA). Our method adeptly addresses the accuracy and efficiency requirements for long-sequence AMC. Specifically, we introduce the Selective State Space Model as the backbone, enhancing the model efficiency by reducing the dimensions of the state matrices and diminishing the frequency of information exchange across GPU memories. We design a denoising-capable unit to elevate the network's performance under low signal-to-noise radio. Rigorous experimental evaluations on the publicly available dataset RML2016.10, along with our synthetic dataset within multiple quadrature amplitude modulations and lengths, affirm that MAMCA delivers superior recognition accuracy while necessitating minimal computational time and memory occupancy. Codes are available on https://github.com/ZhangYezhuo/MAMCA.

Specific Emitter Identification Handling Modulation Variation with Margin Disparity Discrepancy

In the domain of Specific Emitter Identification (SEI), it is recognized that transmitters can be distinguished through the impairments of their radio frequency front-end, commonly referred to as Radio Frequency Fingerprint (RFF) features. However, modulation schemes can be deliberately coupled into signal-level data to confound RFF information, often resulting in high susceptibility to failure in SEI. In this paper, we propose a domain-invariant feature oriented Margin Disparity Discrepancy (MDD) approach to enhance SEI's robustness in rapidly modulation-varying environments. First, we establish an upper bound for the difference between modulation domains and define the loss function accordingly. Then, we design an adversarial network framework incorporating MDD to align variable modulation features. Finally, We conducted experiments utilizing 7 HackRF-One transmitters, emitting 11 types of signals with analog and digital modulations. Numerical results indicate that our approach achieves an average improvement of over 20\% in accuracy compared to classical SEI methods and outperforms other UDA techniques. Codes are available at https://github.com/ZhangYezhuo/MDD-SEI.