Mississippi State University
Ball, John E.
Date of Degree
Original embargo terms
Graduate Thesis - Open Access
Electrical & Computer Engineering
Master of Science
James Worth Bagley College of Engineering
Department of Electrical and Computer Engineering
Radio-frequency (RF) fingerprinting is a process that uses the minute inconsistencies among manufactured radio transmitters to identify wireless devices. Coupled with location fingerprinting, which is a machine learning technique to locate devices based on their radio signals, it can uniquely identify and locate both trusted and rogue wireless devices transmitting over the air. This can have wide-ranging applications for the Internet of Things, security, and networking fields. To contribute to this effort, this research first builds a software-defined radio (SDR) testbed to collect an RF dataset over LTE and WiFi channels. The developed testbed consists of both hardware which are receivers with multiple antennas and software which performs signal preprocessing. Several features that can be used for RF device fingerprinting and location fingerprinting, including received signal strength indicator and channel state information, are also extracted from the signals. With the developed dataset, several data-driven machine learning algorithms have been implemented and tested for fingerprinting performance evaluation. Overall, experimental results show promising performance with a radio fingerprinting accuracy above 90\% and device localization within 1.10 meters.
National Institute of Justice (NIJ) grant 2018-75-CX-K002
Smith, Nicholas G., "Radio frequency dataset collection system development for location and device fingerprinting" (2021). Theses and Dissertations. 5146.