Radio Frequency (RF) Fingerprinting is the theory of identifying a wireless device based on its unique transmitting characteristics which can improve authentication and security in wireless networks. This research project will briefly discuss RF Fingerprinting, Machine Learning, and implement and compare both deep learning and traditional learning techniques for RF Fingerprinting.
Reference:
Otto, A., Rananga, S. & Masonta, M.T. 2023. Work in Progress: Deep learning vs. traditional learning for radio frequency fingerprinting. http://hdl.handle.net/10204/13663 .
Otto, A., Rananga, S., & Masonta, M. T. (2023). Work in Progress: Deep learning vs. traditional learning for radio frequency fingerprinting. http://hdl.handle.net/10204/13663
Otto, AJ, S Rananga, and Moshe T Masonta. "Work in Progress: Deep learning vs. traditional learning for radio frequency fingerprinting." Southern Africa Telecommunication Networks and Applications Conference (SATNAC) 2023, Champagne Sports Resort, 27 - 29 August 2023 (2023): http://hdl.handle.net/10204/13663
Otto A, Rananga S, Masonta MT, Work in Progress: Deep learning vs. traditional learning for radio frequency fingerprinting; 2023. http://hdl.handle.net/10204/13663 .
ResearchSpace
