Module


General information
Kanalcodierung
Channel Coding
MK113
Prof. Dr. Badri-Höher, Sabah (sabah.badri-hoeher@fh-kiel.de)
Prof. Dr. Badri-Höher, Sabah (sabah.badri-hoeher@fh-kiel.de)
Wintersemester 2018/19
1 Semester
In der Regel im Wintersemester
Englisch
Curricular relevance (according to examination regulations)
Study Subject Study Specialization Study Focus Module type Semester
M.Sc. - MIE - Information Engineering Information Technology and Systems Wahlmodul
M.Sc. - MIE - Information Engineering Business IT-Management Wahlmodul
M.Sc. - MIE - Information Engineering Intelligent Systems Wahlmodul
M.Eng. - MET - Elektrische Technologien Kommunikationstechnik und Embedded Systems Wahlmodul
M.Sc. - MIE - Information Engineering IT Security Wahlmodul

Qualification outcome
Areas of Competence: Knowledge and Understanding; Use, application and generation of knowledge; Communication and cooperation; Scientific self-understanding / professionalism.
After successful completion of this module, the students will be able to perform error detection and error correction in digital transmissions schemes and digital storage systems. Furthermore, the students will be able to perform channel encoding and channel decoding.
The students will be capable to distinguish between different code families, particularly block codes and convolutional codes. Furthermore, they will be able to perform suitable decoding methods, like syndrome decoding for block codes and Viterbi decoding for convolutional codes. Additionally, they can construct serial and parallel concatenated codes and use them in digital systems.

In lab experiments, the students will emulate data transmission. They will model channel coding schemes and design suitable decoding methods in order to perform error detection and error correction. They will exploit different decoding schemes (hard-decision vs soft-decision decoding, maximum-likelihood decoding, Viterbi algorithm). The students will be able to measure bit error rates and to evaluate the decoders in different simulation environments.
Due to group-wise problem solving with typically just two students per group, problems can be solved efficiently. Soft skills like communication skills will be trained. The students will learn to split complex problems into sub-tasks and to join the corresponding sub-results.
Content information
Block codes (SPC, Hamming, BSH, CRC, RS, LDPC): Properties, parameters.
Convolutional codes: Description, state diagramm, trellis diagramm.
Decoding : Hard- and Soft-decoding, Syndrom-decoding, ML-decoding, Viterbi-algorithm.
Concatenated codes:
- Serial concatenation and their decoding
- Parallel concatenation (Turbo codes)
- E. Biglieri, Coding for Wireless Channels. Springer, 2005.
- J.G. Proakis, Digital Communication. McGraw-Hill, New York, 1995.
- .M. Bossert, Channel Coding for Telecommunications, John Wiley & Sons, 1999.
- P.M. Gray, Source Coding Theory. Kluwer Academic Publishers, 1998.
- J.C.A Van der Lubbe, Information Theory. Cambridge University, 1988.
- R. Veldhuis, Intorduction to Source Coding. Prentice Hall, UK, 1993.
Teaching formats of the courses
Teaching format SWS
Labor 1
Lehrvortrag 2
Übung 1
Workload
4 SWS
5,0 Credits
48 Hours
102 Hours
Module Examination
Method of Examination Duration Weighting gem. PVO §11 Satz 3 anrechenbar Graded Remark
Klausur 90 Minutes 80 %
Bericht 20 %