In this paper, we enhance the robustness of deep-learning-based radio fingerprinting from three aspects, including parameter selection in pre-processing, ...

Abstract—Radio fingerprinting identifies wireless devices by leveraging hardware imperfections embedded in radio frequency. (RF) signals.

We leverage fine-tuning to improve the robustness of deep-learning-based radio fingerprinting. This repository contains GNU Radio source code for receivers and ...

Hardware imperfections (I/Q imbalance, phase noise, nonlinear distortion, etc.) lead to minor shifts in RF signals. • Each transmitter has unique hardware ...

Jun 28, 2021 · This study proposes to leverage fine-tuning to improve the robustness of radio fingerprinting in a cross-day scenario, where training and test I/Q data are ...

This approach leverages the U-Net framework, training NN using the MSE between the generated RM and the ground truth, yielding impressive results [17].

Missing: RadioNet: | Show results with:RadioNet:

Similarly, Li et al. [15] used a deep learning method and proposed a fine-tuning approach to update the neural network model with new data, improving accuracy.

Robust deep-learning-based radio fingerprinting with fine-tuning. H Li, C Wang, N Ghose, B Wang. Proceedings of the 14th ACM Conference on Security and Privacy ...

“RadioNet: Robust Deep-Learning Based Radio Fingerprinting” IEEE Conference on Communication and Network Security (CNS 2022), Austin, TX, October 3–5, 2022.