[2603.19972] Model-Driven Learning-Based Physical Layer Authentication for Mobile Wi-Fi Devices

Computer Science > Machine Learning arXiv:2603.19972 (cs) [Submitted on 20 Mar 2026] Title:Model-Driven Learning-Based Physical Layer Authentication for Mobile Wi-Fi Devices Authors:Yijia Guo, Junqing Zhang, Yao-Win Peter Hong, Stefano Tomasin View a PDF of the paper titled Model-Driven Learning-Based Physical Layer Authentication for Mobile Wi-Fi Devices, by Yijia Guo and 3 other authors View PDF HTML (experimental) 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 ...