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

arXiv: https://arxiv.org/abs/2603.19972 · HTML: https://mlsanity.com/paper/2603.19972/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 20 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers](https://mlsanity.com/paper/1810.09082/) (2018)
- [A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions](https://mlsanity.com/paper/2406.03820/) (2024)
- [Physical Layer Authentication and Security Design in the Machine Learning Era](https://mlsanity.com/paper/2305.09748/) (2023)
- [Machine Learning For In-Region Location Verification In Wireless Networks](https://mlsanity.com/paper/1812.07853/) (2018)
- [Physical Layer-Based Device Fingerprinting for Wireless Security: From Theory to Practice](https://mlsanity.com/paper/2506.09807/) (2025)
- [Learning The Likelihood Test With One-Class Classifiers for Physical Layer Authentication](https://mlsanity.com/paper/2210.12494/) (2022)
- [One-Class Classification as GLRT for Jamming Detection in Private 5G Networks](https://mlsanity.com/paper/2405.09565/) (2024)
- [Practical Physical Layer Authentication for Mobile Scenarios Using a Synthetic Dataset Enhanced Deep Learning Approach](https://mlsanity.com/paper/2508.20861/) (2025)

## Built on by (most cited first, 2 of 2)

- [Distributed Physical Layer Authentication and Collaborative RSMA in Non-Terrestrial Networks via Graph Reinforcement Learning](https://mlsanity.com/paper/2609.09475/) (2026)
- [Adaptive Distributed Physical-Layer Authentication and Attack Detection in 6G Non-Terrestrial Networks via Causal Meta-Learning](https://mlsanity.com/paper/2609.09511/) (2026)

## Abstract (arXiv)

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.