Addressing the challenges facing deep learning based Specific Emitter Identification via preamble based waveforms

The purpose of this study is to conduct in-depth experiments that analyze the effects on Deep Learning (DL) based Specific Emitter Identification (SEI) and address three issues facing the field. SEI is targeted as a physical-layer security measure that can identify radios within an Internet of Things (IoT) deployment without the need of digital credentials. In the current space, DL SEI is still in its infancy, and has not had the incubation time for a tailor-suited approach to solve issues facing SEI. This thesis introduces methods of improving DL SEI using transforms to allow the networks to learn features that reduce computational cost and improve security. Overall, this thesis highlights the introduction of (i) the natural logarithm as a computationally inexpensive transform of preamble-based waveforms, (ii) assessment of the impacts signal energy has on DL SEI, and (iii) an approach to improving the multi-day classification performance of IEEE 802.11a OFDM emitters.

Grantor dc:publisher

University of Tennessee at Chattanooga

Year dc:date.available

2023

Author dc:creator

• Tyler, Joshua

Contributors dc:contributor

• Reising, Donald
• Kaplagoglu, Erkan; Kandah, Farah
• College of Engineering and Computer Science

• Deep learning (Machine learning)
• Radio frequency identification systems

Licence

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/773

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-1948