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[PAST EVENT] Applied Science Dissertation Defense - Margaret Rooney

November 15, 2021
10am - 12pm
Location
Integrated Science Center (ISC), Room 0280
540 Landrum Dr
Williamsburg, VA 23185Map this location
Access & Features
  • Open to the public
Margaret Rooney - PhD Candidate Applied Science

Title: Characterization of Wireless Communications Networks Using Machine Learning and 3D Electromagnetic Wave Propagation Simulations

Abstract: In this work, we employ machine learning, signal identification, and signal classification to infer network processes governing packet transmission in dense, non-cooperative wireless networks. We exploit signal features in radio frequency (RF) transmissions to generate fingerprints that can enable the characterization of transmission events in a non-cooperative cognitive radio network or in a cognitive adaptive electronic attack scenario. In these situations, we have anticipated a need to depend heavily on identifying RF features that correspond to the way in which devices access spectrum channels and to the interactions of transmitted signals with the devices' surroundings. We develop improved signal processing for detection, estimation, and RF fingerprinting of wireless communications, and employ machine learning techniques for interpretation and classification of complex signals. We then use high-performance computing to create models and simulations of RF interactions with the environment to augment our study of the effects of scatterers in urban environments on the operations of communications networks due to mobility, multipath, absorption, and diffraction.

Bio: Margaret Rooney earned a B.S. in Mathematics from St. John's University, Jamaica, NY in 2016. She began her graduate studies later that year as part of the Nondestructive Evaluation Lab under the guidance of Dr. Mark Hinders, and earned a M.S. in Applied Science in 2018. Her research focuses on two areas: signal detection and identification in densely populated wireless networks, and characterization of the interactions between high frequency radio waves and the environment. Her research interests include machine learning, cognitive radio, and 5G communications.