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.