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

TL;DR

This paper introduces LiteNP-Net, a learning-based physical layer authentication scheme for Wi-Fi IoT devices that approaches optimal detection performance without prior channel knowledge, validated through extensive simulations and real-world experiments.

Contribution

The paper develops LiteNP-Net, a lightweight neural network driven by hypothesis testing, which achieves near-optimal physical layer authentication performance without requiring channel statistics.

Findings

LiteNP-Net approaches the Neyman-Pearson detector performance.

LiteNP-Net outperforms conventional correlation-based methods.

Experimental results confirm effectiveness in real-world Wi-Fi scenarios.

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.