On Monday, 26th of Oct. 2020, 09:00 AM, M.Sc. student “Ali Abdullah Bashkh” defended his thesis entitled:
“ Self-Learning Blind Wireless Channel Identifier Using Adaptive Algorithms”
The discussion committee included:
Prof. Dr.Amjad Khalil Hamidi / University of Technology / Control and Systems Engineering / Chairman.
Assistant Prof.Dr. Fadel Sahib Abbas / Al-Mustansiriya University / College of Engineering/member.
Dr. Muhammad Emad Abdul Sattar / College of Information Engineering / Member.
Dr.Sami Kazem Hasan / College of Information Engineering / Member and supervisor.
The thesis was accepted with minor corrections within a period of one month. and the student fulfilled the requirements for obtaining a master’s degree. (very good).
The adaptive filter adapts its coefficients according to the error between its output and the received signal samples. The adaptive filters have various applications such as channel identification, equalization, noise cancellation, and prediction. Blind wireless channel identification is the scope of this thesis. In this thesis, blind adaptive algorithms (least mean squares (LMS), normalized least mean squares (NLMS), recursive least squares (RLS), constant modulus algorithm (CMA) and Sato) are proposed to address the problem of blind adaptive wireless channel identification and provide good adaptive filtering performance. Expressions to update the adaptive filter coefficients are derived. These algorithms are developed to provide comparisons in the convergence speed, mean squared errors (MSE), and complexity.
Different filter tap-weights and different signal to noise ratio (SNR) values are used to provide comparable results. The performance of the proposed algorithms is analyzed and simulation results showed that the RLS algorithm has the fastest convergence and less MSE value compared to other proposed adaptive algorithms. Finally, the results showed that as the adaptive filter length increases, the convergence of its coefficients to the optimum values becomes slower.