Search result: Catalogue data in Autumn Semester 2020

Electrical Engineering and Information Technology Master Information
Master Studies (Programme Regulations 2018)
The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Communication", see Link.

The individual study plan is subject to the tutor's approval.
Core Courses
These core courses are particularly recommended for the field of "Communication".
You may choose core courses form other fields in agreement with your tutor.

A minimum of 24 credits must be obtained from core courses during the MSc EEIT.
Foundation Core Courses
Fundamentals at bachelor level, for master students who need to strengthen or refresh their background in the area.
227-0121-00LCommunication Systems Information W6 credits2V + 2UA. Wittneben
AbstractInformation Theory, Signal Space Analysis, Baseband Transmission, Passband Transmission, Example und Channel, Data Link Layer, MAC, Example Layer 2, Layer 3, Internet
ObjectiveIntroduction into the fundamentals of digital communication systems. Selected examples on the application of the fundamental principles in existing and upcoming communication systems
ContentCovered are the lower three layer of the OSI reference model: the physical, the data link, and the network layer. The basic terms of information theory are introduced. After this, we focus on the methods for the point to point communication, which may be addressed elegantly and coherently in the signal space. Methods for error detection and correction as well as protocols for the retransmission of perturbed data will be covered. Also the medium access for systems with shared medium will be discussed. Finally, algorithms for routing and flow control will be treated.

The application of the basic methods will be extensively explained using existing and future wireless and wired systems.
Lecture notesLecture Slides
Literature[1] Simon Haykin, Communication Systems, 4. Auflage, John Wiley & Sons, 2001
[2] Andrew S. Tanenbaum, Computernetzwerke, 3. Auflage, Pearson Studium, 2003
[3] M. Bossert und M. Breitbach, Digitale Netze, 1. Auflage, Teubner, 1999
227-0101-00LDiscrete-Time and Statistical Signal Processing Information W6 credits4GH.‑A. Loeliger
AbstractThe course introduces some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, inverse filters and equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm.
ObjectiveThe 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. A recurrent theme throughout the course is the stable and robust "inversion" of a linear filter.
Content1. Discrete-time linear systems and filters:
state-space realizations, z-transform and spectrum,
decimation and interpolation, digital filter design,
stable realizations and robust inversion.

2. The discrete Fourier transform and its use for digital filtering.

3. The statistical perspective:
probability, random variables, discrete-time stochastic processes;
detection and estimation: MAP, ML, Bayesian MMSE, LMMSE;
Wiener filter, LMS adaptive filter, Viterbi algorithm.
Lecture notesLecture Notes
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