Model-Driven Learning-Based Physical Layer Authentication for Mobile Wi-Fi Devices

Guo, Y ORCID: 0000-0001-7098-6926, Zhang, J ORCID: 0000-0002-3502-2926, Hong, PWP ORCID: 0000-0001-7043-8276 and Tomasin, S ORCID: 0000-0003-3253-6793 (2026) Model-Driven Learning-Based Physical Layer Authentication for Mobile Wi-Fi Devices IEEE Transactions on Information Forensics and Security, 21. pp. 1497-1511. ISSN 1556-6013, 1556-6021

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Abstract

The rise of wireless technologies has made the Internet of Things (IoT) ubiquitous, but the broadcast nature of wireless communications exposes IoT to authentication risks. Physical layer authentication (PLA) offers a promising solution by leveraging unique characteristics of wireless channels. As a common approach in PLA, hypothesis testing yields a theoretically optimal Neyman-Pearson (NP) detector, but its reliance on channel statistics limits its practicality in real-world scenarios. In contrast, deep learning-based PLA approaches are practical but tend to be not optimal. To address these challenges, we proposed a learning-based PLA scheme driven by hypothesis testing and conducted extensive simulations and experimental evaluations using Wi-Fi. Specifically, we incorporated conditional statistical models into the hypothesis testing framework to derive a theoretically optimal NP detector. Building on this, we developed LiteNP-Net, a lightweight neural network driven by the NP detector. Simulation results demonstrated that LiteNP-Net could approach the performance of the NP detector even without prior knowledge of the channel statistics. To further assess its effectiveness in practical environments, we deployed an experimental testbed using Wi-Fi IoT development kits in various real-world scenarios. Experimental results demonstrated that the LiteNP-Net outperformed the conventional correlation-based method as well as state-of-the-art Siamese-based methods.

Item Type:	Article
Uncontrolled Keywords:	Authentication, Detectors, Wireless communication, Mathematical models, Neural networks, Internet of Things, Wireless fidelity, Symbols, Learning systems, Communication system security, physical layer authentication, channel state information, hypothesis test, neural networks
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Computer Science & Informatics Faculty of Science & Engineering > School of Computer Science & Informatics > Trustworthy Computing
Depositing User:	Symplectic Admin
Date Deposited:	19 Jan 2026 10:59
Last Modified:	28 Feb 2026 14:58
DOI:	10.1109/TIFS.2026.3657184
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3196665
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