Stochastic Signals and Telecommunications

Part A: Basic theory and techniques. Overview of stochastic processes. Elements of estimation and detection theory. Emphasis is given on second order estimators, the Wiener estimator and the Kalman Estimator. Recursive estimation techniques, Basic recursive estimation algorithms, Spectrum analysis; Non parametric techniques (Periodogram, Bartlett method), Parametric techniques (e.g. AR models). Part B: Presentation of selected applications in Signal Processing and Communications, Channel estimation, Channel Equalization, Symbol Synchronization Algorithms, Spatial Filtering, Smart Antennas.

Objectives

Acquiring basic knowledge about stochastic processes. Connection of theory with practical application for better understanding of the capabilities and applicability of random processes.

Prerequisites

Linear Algebra Probability Digital Signal Processing

Syllabus

Introduction - Description of the material Basic elements of Linear Algebra Discrete-Time Random Processes Signal Modeling Optimal Wiener Filters Adaptive Algorithms Application 1: Spectrum estimation Application 2: Smart Antennas Application 3: Channel Equalization Application 4: Symbol Synchronization