A. Lectures During the course, the following material, among others, will be covered:
- Overview of basic principles of stochastic processes.
- Elements of detection theory.
- Elements of estimation theory: Parameters.
- Elements of estimation theory: Signals.
- Focus on estimators utilizing second-order statistics, Wiener Estimator.
- Recursive estimation techniques, basic recursive algorithms.
- Power spectrum estimation.
- Space-time processing with constraints (LCMV)
- Examples: o Smart antennas. Beamforming. DoA estimation. o Blind system identification. o Channel estimation and equalization.
- Elements of the theory of statistical learning.
- Basic methods for supervised learning
- Basic methods for unsupervised learning
B. Laboratory exercises
- Exercise 1: Implementation and comparative performance study of power spectrum estimation techniques.
- Exercise 2: Implementation and performance study of system identification techniques.
- Exercise 3: Implementation and performance study of channel estimation and equalization techniques.
- Exercise 4: Implementation of adaptive algorithms for time varying systems.
- Exercise 5: Implementation and performance study of techniques for supervised and unsupervised learning.
