Advanced Topics on Telecommunications

Course Code: 
Winter Semester
Credit Points: 

Course outline

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.

Startup Growth Lite is a free theme, contributed to the Drupal Community by More than Themes.