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ELEC Oral Comprehensive Exam for Doctoral Candidacy by Todd Matthew Morehouse Jr.

When: Thursday, December 22, 2022
10:00 AM - 12:00 PM
Where: > See description for location
Cost: Free
Description: Topic: A Machine Learning-enabled Intelligent, Adaptive, and Autonomous Radio System

Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A

Zoom Conference Link: Zoom Link
Meeting ID: 997 4288 6665
Passcode: 391787

Wireless communications continue to see advancements from machine learning (ML) and artificial intelligence (AI). Typically, this is applied to individual components, attempting to add new capabilities, or enhance old ones. Little research has been done in the combination of these different components, to create a complete intelligent radio. This dissertation proposal aims to further the research of these components, and to combine these into an intelligent radio system. The proposed system intends to perform spectrum sensing, channel allocation, and software-defined transceiver signal processing, all enabled by ML. The goal of the design is to progress research into applying ML to these tasks in wireless systems. Specifically, Faster Region-based Convolutional Neural Network (FRCNN) along with open world learning will be leveraged in spectrum sensing and signal characterization. Multiple signals in cluttered RF environments will be simultaneously localized and characterized, including new-to-the-network signals by designing novelty detection and incremental learning using 1-D baseband raw data. The results of the spectrum sensing algorithm will be used to separate multiple signals in time domain. ML will be applied to enhance the signals by reducing channel effects. These enhanced signals are then demodulated using a deep learning approach. Finally, reinforcement learning is used to reconfigure the RF frontend, by allocating part of the wireless spectrum to transmit over. Such an intelligent radio system is anticipated to advance end-to-end communications, spectrum awareness, and electronic warfare.

Advisor(s): Dr. Ruolin Zhou, Assistant Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth

Committee Members:
Dr. Dayalan Kasilingam, Professor and Chairperson, Department of Electrical & Computer Engineering, UMASS Dartmouth
Dr. Honggang Wang, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth
Dr. Vasu Chakravarthy, AFRL Fellow, Principal Electronic Engineer, Cognitive EW Research Lead

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 at 508.910.6922 or via email at
Contact: > See Description for contact information
Topical Areas: General Public, University Community, College of Engineering, Electrical and Computer Engineering