Module: Information Theory and Coding
|Information Theory and Coding||Lecture||3||Summer Semester|
|Information Theory and Coding||Recitation Section (large)||1||Summer Semester|
Prof. Gerhard Bauch
Recommended Previous Knowledge:
- Mathematics 1-3
- Probability theory and random processes
- Basic knowledge of communications engineering (e.g. from lecture "Fundamentals of Communications and Random Processes")
The students know the basic definitions for quantification of information in the sense of information theory. They know Shannon's source coding theorem and channel coding theorem and are able to determine theoretical limits of data compression and error-free data transmission over noisy channels. They understand the principles of source coding as well as error-detecting and error-correcting channel coding. They are familiar with the principles of decoding, in particular with modern methods of iterative decoding. They know fundamental coding schemes, their properties and decoding algorithms.
The students are able to 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. They can estimate the parameters of an error-detecting or error-correcting channel coding scheme for achieving certain performance targets. They are able to compare the properties of basic channel coding and decoding schemes regarding error correction capabilities, decoding delay, decoding complexity and to decide for a suitable method. They are capable of implementing basic coding and decoding schemes in software.
The students can jointly solve specific problems.
The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system.
ECTS-Credit Points Module:
Workload in Hours:
Independent Study Time: 124, Study Time in Lecture: 56
Course: Information Theory and Coding
German & English
Fundamentals of information theory
Self information, entropy, mutual information
Source coding theorem, channel coding theorem
Channel capacity of various channels
Fundamental source coding algorithms:
Huffman Code, Lempel Ziv Algorithm
Fundamentals of channel coding
Basic parameters of channel coding and respective bounds
Decoding principles: Maximum-A-Posteriori Decoding, Maximum-Likelihood Decoding, Hard-Decision-Decoding and Soft-Decision-Decoding
Low Density Parity Check (LDPC) Codes and iterative Ddecoding
Convolutional codes and Viterbi-Decoding
Turbo Codes and iterative decoding
Bossert, M.: Kanalcodierung. Oldenbourg.
Friedrichs, B.: Kanalcodierung. Springer.
Lin, S., Costello, D.: Error Control Coding. Prentice Hall.
Roth, R.: Introduction to Coding Theory.
Johnson, S.: Iterative Error Correction. Cambridge.
Richardson, T., Urbanke, R.: Modern Coding Theory. Cambridge University Press.
Gallager, R. G.: Information theory and reliable communication. Whiley-VCH
Cover, T., Thomas, J.: Elements of information theory. Wiley.