Semester Award Granted
Spring 2025
Submission Date
May 2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Thesis/Dissertation Advisor [Chair]
Georgios Sklivanitis
Abstract
As 5G networks expand, ensuring secure identification and authentication of user devices is critical. This paper explores machine learning-based Radio Frequency Fingerprinting (RFF) to identify and distinguish trusted and rogue devices in 5G networks. We evaluate ResNet, Transformer, and LSTM architectures using channel-isolated (CI) spectrogram and raw IQ slice inputs across varying packet sizes. Results show that ResNet with CI spectrogram inputs achieves the highest device classification accuracy and scalability while mitigating the Next-Day Effect, while the same architecture with IQ Slices is best for rogue device detection. Unlike related works, we emphasize the role of spectrograms in accurately capturing discerning features in 5G signals for scalable RFF applications. These fingerprints strengthen authentication processes against device impersonation at the physical layer of 5G networks. Using real-world 5G datasets from an outdoor wireless network testbed, this study demonstrates the feasibility of AI-driven RFF for secure device authentication.
Recommended Citation
Jawne, Aishwarya, "AI-ASSISTED RADIO FREQUENCY FINGERPRINTING FOR IDENTIFICATION OF USER DEVICES IN 5G NETWORKS" (2025). Electronic Theses and Dissertations. 61.
https://digitalcommons.fau.edu/etd_general/61