On Tuesday, June 17, 2025, a Master’s thesis titled “Machine Learning-Enabled Signal Processing for Communication Systems” was defended by student Manar Talal Muhammad Ali at the College of Electronics Engineering, Department of Computer and Informatics Engineering, University of Nineveh.
The research was carried out in line with the directives of Prof. Dr. Osama Al-Mashhadani, President of the University of Nineveh, to promote international academic collaboration. The study was conducted in partnership with Nottingham Trent University (NTU) in the United Kingdom. The thesis defense was attended by Prof. Dr. Jaafar Ramadan Muhammad, Assistant University President for Scientific Affairs, and Prof. Dr. Khaled Khalil Muhammad, Dean of the College of Electronics Engineering.
The thesis examined In-Band Full-Duplex (IBFD) communication as a promising solution to enhance spectral efficiency and data throughput, particularly in bandwidth-constrained environments. The study focused on applying Machine Learning (ML)-based Self-Interference Cancellation (SIC) techniques to effectively mitigate the Self-Interference (SI) inherent in IBFD systems.
The research concluded that ML-based SIC techniques achieved notable improvements in interference cancellation rates and spectral efficiency, resulting in enhanced overall system performance when compared to conventional techniques.
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Thesis Committee
1. Assist. Prof. Dr. Muhammad Hazem Al-Jammas – Chair
2. Assist. Prof. Dr. Sidqi Bakr Dhannon – Member
3. Assist. Prof. Dr. Ahmed Muhammad Ahmed Salama – Member
4. Assist. Prof. Dr. Bilal Alaa Al-Din Jabr – Member & Primary Supervisor
(All from the University of Nineveh / College of Electronics Engineering)
5. Prof. Dr. Charalambos Tsimenidis – Nottingham Trent University, UK (Co-Supervisor)
