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227-0101-00L 6 Credits BSC , MSC D-ITET , D-MATH
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Stochastic Models and Signal Processing

Stochastische Modelle und Signalverarbeitung

Lecturers & Examiners: Prof. Dr. Hans-Andrea Loeliger
VVZ CR 3.4

Last Updated: 2026-02-05 15:24:15

Abstract

The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm.

Objective

The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are "linearity" and "probability". In the first part of the course, we deepen our understanding of discrete-time linear filters. In the second part of the course, we review the basics of probability theory and discrete-time stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm.

Content

Discrete-time linear systems and the z-transform. Discrete time and continuous time: forth and back. Digital filters. DFT. Elements of probability theory. Discrete-time stochastic processes. Elements of detection theory and estimation theory. Linear estimation and filtering. Wiener filter. LMS algorithm. Viterbi algorithm.

Resources

Lecture Notes

Lecture Notes.

General Information

Language
German
Levels
BSC , MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 180 minutes
Aids
Vorlesungsskript, ein A4-Ordner mit beliebigen Notizen sowie Taschenrechner

Course Components

Type Title Time & Place Hours
lecture with exercise Stochastische Modelle und Signalverarbeitung
  • Tue 13:15-17:00 (ETF C 1)
4 h weekly

Offered In