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Proceedings of the AAAI Conference on Artificial Intelligence

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Towards Distance-Invariant Radio Frequency Fingerprinting...
Shiyue Huang · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Shiyue Huang University of Electronic Science and Technology of China
  • Yuchen Su University of Electronic Science and Technology of China
  • Hongbo Liu University of Electronic Science and Technology of China
  • Zikang Ding University of Electronic Science and Technology of China
  • Xuewan He University of Electronic Science and Technology of China
  • Yanzhi Ren University of Electronic Science and Technology of China
  • Haitao Jia University of Electronic Science and Technology of China

DOI:

https://doi.org/10.1609/aaai.v40i1.37005

Abstract

Radio Frequency Fingerprinting (RFF) exploits inherent hardware-level imperfections of wireless transmitters as unclonable identifiers for device identification. These unique signatures, concealed in transmitted signals, inevitably experience complex distortions during wireless propagation (i.e., coupled with ambient noise and channel fading), making it extremely challenging for reliable extraction. Despite substantial research efforts dedicated to advancing effective fingerprint extraction techniques, current approaches still struggle in handling fingerprint robustness under distance variations, leading to severe SNR fluctuations and complex multipath effects. To address this gap, we propose the first unsupervised framework for distance-invariant radio frequency fingerprinting, eliminating dependence on labeled target domain data. Specifically, we first preprocess raw RF samples by confining them within a specified variation range and filtering noisy high-frequency components while avoiding aliasing. For source domain data, we then propose a set of physics-inspired data augmentation techniques designed to emulate realistic wireless signal propagation effects. Building on this, we introduce a dual alignment contrastive learning method to explicitly decouple identity-discriminative features, ensuring the model focuses on device-specific traits. Furthermore, we incorporate a pseudo-labeling-based domain adaptation module to refine the model for the unlabeled target domain, enhancing its generalization to unseen distances. Extensive experiments on public datasets show that our method achieves the identification accuracy outperforming state-of-the-art approaches by 40%, while maintaining computational efficiency suitable for edge deployment.

How to Cite

Huang, S., Su, Y., Liu, H., Ding, Z., He, X., Ren, Y., & Jia, H. (2026). Towards Distance-Invariant Radio Frequency Fingerprinting via Augmented Unsupervised Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 426–434. https://doi.org/10.1609/aaai.v40i1.37005

Issue

Section

AAAI Technical Track on Application Domains I

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