Aishwarya Jawne (Center for Connected Autonomy & AI, Florida Atlantic University), Georgios Sklivanitis (Center for Connected Autonomy & AI, Florida Atlantic University), Dimitris A. Pados (Center for Connected Autonomy & AI, Florida Atlantic University), Elizabeth Serena Bentley (Air Force Research Laboratory)

As 5G networks expand to support increasingly complex and diverse applications, ensuring robust identification and authentication of user devices has become a critical requirement for physical layer security. This paper investigates the potential of machine learning techniques for radio frequency (RF) fingerprinting as a scalable solution for identifying and authorizing access to trusted user devices as well as detecting rogue user devices in 5G networks. Specifically, we evaluate the performance of three prominent deep learning architectures— ResNet, Transformer, and LSTM — across various configurations, including spectrogram and raw IQ slice inputs made from varying packet sizes. The results demonstrate that ResNet models, when paired with spectrogram inputs, achieve the highest classification accuracy and scalability, while effectively addressing challenges such as the Next-Day Effect. Contrary to existing works, which focus on training deep neural networks (DNNs) for device classification, we highlight the critical role of spectrograms in capturing distinct hardware impairments when used to train DNNs for RF fingerprint extraction. These RF fingerprints are then used to distinguish between trusted and rogue 5G devices, as well as for device classification and identification. By identifying the optimal configurations for these tasks and exploring their applicability to real-world datasets collected from an outdoor software-defined radio testbed, this paper provides a pathway for integrating AI-driven radio frequency fingerprinting for authentication of user devices in 5G and FutureG networks as a cornerstone for enhanced physical layer security.

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Xuanji Meng (Tsinghua University), Xiao Sui (Shandong University), Zhaoxin Yang (Tsinghua University), Kang Rong (Blockchain Platform Division,Ant Group), Wenbo Xu (Blockchain Platform Division,Ant Group), Shenglong Chen (Blockchain Platform Division,Ant Group), Ying Yan (Blockchain Platform Division,Ant Group), Sisi Duan (Tsinghua University)

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Maximilian von Tschirschnitz (Technical University of Munich), Ludwig Peuckert (Technical University of Munich), Moritz Buhl (Technical University of Munich), Jens Grossklags (Technical University of Munich)

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Tongxin Wei (Nankai University), Ding Wang (Nankai University), Yutong Li (Nankai University), Yuehuan Wang (Nankai University)

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Fengchen Yang (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Zihao Dan (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Kaikai Pan (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Chen Yan (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Xiaoyu Ji (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Wenyuan Xu (Zhejiang University; ZJU…

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