Mr. Saidur Pavel has successfully defended his Ph.D. dissertation, “Array Processing with Compressive Measurements and Coherent Signals,” on April 6, 2026.
Saidur’s dissertation addresses two important areas: Hybrid Beamforming for Massive Multiple-Input Multiple-Output (MIMO) Systems and Direction-of-Arrival (DOA) Estimation for Coherent Signals. For the former, Saidur developed advanced optimization techniques for hybrid beamforming by introducing deep learning–based frameworks, including fully connected neural networks, long short-term memory (LSTM) models, and reinforcement learning approaches, to optimize compressive measurement matrices considering variety of operational environments. For the latter, Saidur developed innovative algorithms that exploit the structure of covariance matrices and tensor representations to effectively decorrelate coherent signals, enabling accurate and robust DOA estimation even under challenging conditions.
Saidur’s Ph.D. studies were supported by the Temple University Presidential Fellowship, the National Science Foundation, and the Air Force Office of Scientific Research (AFOSR).
More details aout Saidur’s PhD experience can be found in “Saidur Pavel: A PhD Journey in Research Excellence.” His dissertation “Array Processing With Compressive Measurements and Coherent Signals” is available in the ProQuest website, and he maintains a personal website at https://saidurpavel.com/.