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
Advanced Core Courses
Advanced core courses bring students to gain in-depth knowledge of the chosen specialization. They are MSc level only.
227-0301-00LOptical Communication FundamentalsW6 credits2V + 1U + 1PJ. Leuthold
AbstractThe path of an analog signal in the transmitter to the digital world in a communication link and back to the analog world at the receiver is discussed. The lecture covers the fundamentals of all important optical and optoelectronic components in a fiber communication system. This includes the transmitter, the fiber channel and the receiver with the electronic digital signal processing elements.
ObjectiveAn in-depth understanding on how information is transmitted from source to destination. Also the mathematical framework to describe the important elements will be passed on. Students attending the lecture will further get engaged in critical discussion on societal, economical and environmental aspects related to the on-going exponential growth in the field of communications.
Content* Chapter 1: Introduction: Analog/Digital conversion, The communication channel, Shannon channel capacity, Capacity requirements.

* Chapter 2: The Transmitter: Components of a transmitter, Lasers, The spectrum of a signal, Optical modulators, Modulation formats.

* Chapter 3: The Optical Fiber Channel: Geometrical optics, The wave equations in a fiber, Fiber modes, Fiber propagation, Fiber losses, Nonlinear effects in a fiber.

* Chapter 4: The Receiver: Photodiodes, Receiver noise, Detector schemes (direct detection, coherent detection), Bit-error ratios and error estimations.

* Chapter 5: Digital Signal Processing Techniques: Digital signal processing in a coherent receiver, Error detection teqchniques, Error correction coding.

* Chapter 6: Pulse Shaping and Multiplexing Techniques: WDM/FDM, TDM, OFDM, Nyquist Multiplexing, OCDMA.

* Chapter 7: Optical Amplifiers : Semiconductor Optical Amplifiers, Erbium Doped Fiber Amplifiers, Raman Amplifiers.
Lecture notesLecture notes are handed out.
LiteratureGovind P. Agrawal; "Fiber-Optic Communication Systems"; Wiley, 2010
Prerequisites / NoticeFundamentals of Electromagnetic Fields & Bachelor Lectures on Physics.
227-0417-00LInformation Theory IW6 credits4GA. Lapidoth
AbstractThis course covers the basic concepts of information theory and of communication theory. Topics covered include the entropy rate of a source, mutual information, typical sequences, the asymptotic equi-partition property, Huffman coding, channel capacity, the channel coding theorem, the source-channel separation theorem, and feedback capacity.
ObjectiveThe fundamentals of Information Theory including Shannon's source coding and channel coding theorems
ContentThe entropy rate of a source, Typical sequences, the asymptotic equi-partition property, the source coding theorem, Huffman coding, Arithmetic coding, channel capacity, the channel coding theorem, the source-channel separation theorem, feedback capacity
LiteratureT.M. Cover and J. Thomas, Elements of Information Theory (second edition)
227-0427-00LSignal Analysis, Models, and Machine Learning
Does not take place this semester.
This course has been replaced by "Introduction to Estimation and Machine Learning" (autumn semester) and "Advanced Signal Analysis, Modeling, and Machine Learning" (spring semester).
W6 credits4GH.‑A. Loeliger
AbstractMathematical methods in signal processing and machine learning.
I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity.
II. Learning linear and nonlinear functions and filters: neural networks, kernel methods.
III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, Gaussian models with sparse events.
ObjectiveThe course is an introduction to some basic topics in signal processing and machine learning.
ContentPart I - Linear Signal Representation and Approximation: Hilbert spaces, least squares and LMMSE estimation, projection and estimation by linear filtering, learning linear functions and filters, L2 regularization, L1 regularization and sparsity, singular-value decomposition and pseudo-inverse, principal-components analysis.
Part II - Learning Nonlinear Functions: fundamentals of learning, neural networks, kernel methods.
Part III - Structured Statistical Models and Message Passing Algorithms: hidden Markov models, factor graphs, Gaussian message passing, Kalman filter and recursive least squares, Monte Carlo methods, parameter estimation, expectation maximization, linear Gaussian models with sparse events.
Lecture notesLecture notes.
Prerequisites / NoticePrerequisites:
- local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.)
- others: solid basics in linear algebra and probability theory
227-0439-00LWireless Access Systems Information W6 credits2V + 2UA. Wittneben
AbstractThe lecture course covers current and upcoming wireless systems for data communication and localization in diverse applications. Important topics are broadband data networks, indoor localization, internet-of-things, biomedical sensor networks and smart grid communications. The course consists of two tracks, the lecture part “Technology & Systems” and the group exercise part “Simulate & Practice”.
ObjectiveGeneral learning goals of the course:
By the end of this course, students will be able to

- understand and illustrate the physical layer and MAC layer limits and challenges of wireless systems with emphasis on data communication and localization
- understand and explain the functioning of the most widely used wireless systems
- model and simulate the physical layer of state-of-the-art wireless systems
- explain challenges and solutions of indoor localization
- understand research challenges of future wireless networks

Specific learning goals include:
- Understanding the principles of OFDM and analyzing its performance on the physical layer
- Understanding and evaluating the challenges regarding current applications of wireless networks, e.g. for the internet-of-things, smart grid communication, biomedical sensor communication
- Illustrating the characteristics of the wireless channel
- Simulation of localization and user tracking based on wireless systems
- Explaining the basics of smart grid communications approaches (including narrowband PLC, G3-PLC)
Content- Introduction
- Wireless communication: fundamental Physical layer and MAC layer limits and challenges
- Basics of OFDM
- Wireless systems: WiFi / WLAN
- Wireless systems: Bluetooth, RFID (Radio Frequency Identification) and NFC (Near Field Communication)
- Indoor localization based on wireless systems
- Internet-of-things: Challenges and solutions regarding wireless data communication and localization
- Smart grid communications
- Biomedical sensor communication
- Next generation designs (glimpse on current research topics)

The goal of the course is to explain and analyze modern and future wireless systems for data communication and localization. The course covers designs for generic applications (e.g. WiFi, Bluetooth) as well as systems optimized for specific applications (e.g. biomedical sensor networks, smart grid communications).

The course consists of two parallel tracks. The track "Technology&Systems" is structured as regular lecture. In the introduction, we discuss the challenges and potential of wireless access and study some fundamental limits of wireless communications and localization approaches.

The second part of this track is devoted to the most widely used wireless systems, WiFi/WLAN, Bluetooth, RFID, NFC. Furthermore, we study the potential of using existing wireless communication systems for indoor localization.

The third part follows with an introduction to the internet-of-things, where we focus on data communication and localization challenges and solutions in wireless networks with a massive number of nodes. Next, we study communication technologies for the smart grid, which combine wireless as well as power line communication approaches to optimize availability and efficiency.

The track is completed by a comprehensive survey of short-range magneto-inductive micro sensor networks for communication and localization - as a promising technology for biomedical sensor communication (in-body, out-of-body).

In the track "Simulate&Practice" we form student teams to simulate and analyze functional blocks of the physical layer of advanced wireless systems (based on MATLAB simulations). The track includes combination tasks in which different teams combine their functional blocks (e.g. transmitter, receiver) in order to simulate the complete physical layer of a wireless system. The focus is on data communication and localization. The tasks include modeling and simulating of single-carrier systems (as, e.g., used in Bluetooth), multi-carrier OFDM systems (e.g. used in WiFi or power line communication), and indoor localization approaches (e.g. relevant for IoT and sensor networks).
Lecture notesLecture slides are available.
LiteratureWill be announced in the lecture.
Prerequisites / NoticeEnglish
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