Factorized Disentangled Representation Learning for Interpretable Radio Frequency Fingerprint

TL;DR

This paper introduces a novel disentangled representation learning framework for radio frequency fingerprinting, improving interpretability, robustness, and controllability in device identification amidst signal variations.

Contribution

The paper proposes a new DRL framework that explicitly disentangles multiple factors, including RFF, using dedicated modules, enhancing interpretability and downstream task performance.

Findings

Achieves high performance on benchmark datasets

Improves classification accuracy for RFFI

Enables precise control over generated signals

Abstract

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.