ELEC Doctor of Philosophy Dissertation Defense by Charles Montes - ECE
Topic: Machine Learning Optimization for Dynamic Spectrum Awareness
Abstract:
This dissertation proposes a machine learning-based approach focused on improving dynamic spectrum awareness in wireless communications. The approach is comprised of four main components: network optimization with genetic algorithm convolutional neural networks (GACNN), which focuses on optimizing neural network architectures for specific tasks to reduce the complexity and cost of the network optimization process; unsupervised prototype learning, which uses hierarchical clustering and a prototype-based learning objective to estimate signal-to-noise ratio (SNR) regions and perform modulation classification to improve the classification accuracy and the identification of new signal classes; deep neural network explainable AI (DNN XAI), which increases the transparency and interpretability of machine learning models in deep neural networks to ensure compliance with spectrum regulatory standards; and lastly, incremental learning class representation drift, which evaluates the performance of incremental learning methods in baseband modulation classification to establish a continuous learning process that adapts to dynamic environments. By addressing gaps in current spectrum management techniques, these components can improve spectrum utilization, increase machine learning-based communication interpretability, and provide an informed model for future spectrum management strategies.
Advisor(s): Dr. Ruolin Zhou, Associate Professor, Department of Electrical & Computer Engineering, UMass Dartmouth
Committee Members:
Dr. Lance Fiondella, Associate Professor, Department of Electrical & Computer Engineering, UMass Dartmouth;
Dr. Alfa Heryudono, Associate Professor, Department of Mathematics, UMass Dartmouth;
Dr. Eugene Chabot, Engineer, Naval Undersea Warfare Center (NUWC)
NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public.
*For further information, please contact Dr. Ruolin Zhou
Charlton College of Business (CCB), Room 115 / Zoom Link: https://umassd.zoom.us/j/99223130208 Meeting ID: 992 2313 0208 Passcode: 768938
Ruolin Zhou
508.910.6922
ruolin.zhou@umassd.edu