Communication and Media Engineering (CME)

Signal and System Theory

Recommended prior knowledge

• Basic knowledge of mathematics for engineers, in particular complex numbers
• Basic knowledge of communications engineering and signal theory

Teaching Methods Vorlesung
Learning objectives / competencies

Upon successful completion of this module, the student will be able to:

  • classify and describe signals and linear time-invariant (LTI) systems using methods of signal and system theory.
  • apply the fundamental transformations of continuous-time and discrete-time signals and systems. They can describe and analyse deterministic signals and systems mathematically in both time and frequency domain. In particular, they understand the effects in time domain and frequency domain which are caused by the transition of a continuous-time signal to a discrete-time signal.
  • determine the limits of data compression as well as of data transmission through noisy channels and based on those limits to design basic parameters of a transmission scheme
  • compare the properties of basic channel coding and decoding schemes regarding error detection or correction capabilities
Duration 1
SWS 4.0
Classes 60 h
Self-study / group work: 120 h
Workload 180 h
ECTS 6.0
Requirements for awarding credit points

Module exam K120

Credits and Grades

6 CP,  grade 1 ... 5

Responsible Person

Prof. Dr.-Ing. Stephan Pfletschinger

Recommended Semester 1
Frequency jedes Jahr (WS)

Master-Studiengang CME


Information Theory and Coding

Type Vorlesung
Nr. EMI405
SWS 2.0
Lecture Content

Channel coding
• Error detection and correction
• Binary linear block codes
• Hard decoding and soft decoding

Information, Entropy and Redundancy
• Information content
• Entropy of random variables and random vectors

Source Coding
• The source coding theorem
• Shannon-Fano coding
• Huffman coding

Discrete memoryless channels
• Conditional and joint entropy
• Mutual information
• The channel coding theorem

Continuous channel models
• The AWGN channel
• Fading channels


• Stefan. M. Moser, Po-Ning Chen, A Student’s Guide to Coding and Information Theory, Cambridge University Press, 2012.
• Benedetto, S., Biglieri, E., Principles of Digital Transmission, Kluwer Academic, Plenum Publishers, 1999.
• Robert McEliece: The Theory of Information and Coding, Student Edition, Cambridge University Press, 2004.
• David MacKay: Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003.
• Thomas M. Cover, Joy A. Thomas, Elements of Information Theory, Wiley, 2006.

Digital Signals and Systems

Type Vorlesung
Nr. EMI403
SWS 2.0
Lecture Content

• Elementary signals: sine, rectangle, complex exponential, Dirac impulse
• Properties of Signals and Systems: periodicity, orthogonality, signal power and signal energy
• Description of linear time-invariant systems in time and frequency domain: Impulse response, step response and transfer function
• Fourier series, Fourier transform, discrete-time Fourier transform, z-transform
• The Sampling Theorem
• Digital Filters: FIR and IIR, Pole-zero-plot, canonical structures


• Alan V. Oppenheim, Alan S. Willsky: Signals & Systems. Pearson, 2013.
• Alan V. Oppenheim, George V. Verghese: Signals, Systems and Inference. Pearson, 2017.
• John G. Proakis, Dimitros K. Manolakis: Digital Signal Processing. Pearson, 2014.
• Stephan Boyd, Lieven Vandenberghe: Introduction to Applied Linear Algebra. Cambridge University Press, 2018.
• Mark Wickert: Signals & Systems for Dummies. Wiley, 2013.