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.

翻译：无线技术的兴起使物联网（IoT）无处不在，但无线通信的广播特性使物联网面临认证风险。物理层认证（PLA）通过利用无线信道的独特特性，提供了一种有前景的解决方案。作为PLA的常见方法，假设检验能够生成理论上最优的奈曼-皮尔逊（NP）检测器，但其对信道统计特性的依赖限制了其在现实场景中的实用性。相比之下，基于深度学习的PLA方法虽然实用，但往往无法达到最优性能。为解决这些挑战，我们提出了一种由假设检验驱动的学习型PLA方案，并通过Wi-Fi进行了广泛的仿真和实验评估。具体而言，我们将条件统计模型融入假设检验框架中，推导出理论上最优的NP检测器。在此基础上，我们开发了LiteNP-Net——一种由NP检测器驱动的轻量级神经网络。仿真结果表明，即使缺乏信道统计先验知识，LiteNP-Net的性能也能接近NP检测器。为进一步评估其在实际环境中的有效性，我们使用Wi-Fi物联网开发套件，在多种真实场景中搭建了实验测试平台。实验结果表明，LiteNP-Net超越了传统的基于相关性的方法以及当前最先进的基于孪生网络的方法。