Information Theory and Coding (TIINCO)
Lecturer: Qi Zhang
Associate Professor in Aarhus School of Engineerig, Aarhus University
- Language of instruction: English
- Level of course: Graduate course
- Semester/quarter: Q3
- Hours per week: 4
Objectives of the course
The participants will learn the basic concepts of information theory and coding, including information, source coding, channel model, channel capacity, channel coding and so on. The main purpose of this course is to help students to complete the understanding of the wireless communication system with other advanced courses in wireless communication.
Learning outcomes and competences
The participants must at the end of the course be able to:
- Understand and explain the basic concepts of information theory, source coding, channel and channel capacity, channel coding and relation among them.
- Describe the real life applications based on the fundamental theory.
- Calculate entropy, channel capacity, bit error rate, code rate, steady-state probability and so on.
- Implement the encoder and decoder of one block code or convolutional code using any program language.
Since Shannon published his paper on information theory in 1948, the ideas from information theory have been applied to many areas such as wireless communication, video compression, bioinformatics, and others. The applications are endless; however, the beauty of fundamental mathematic theory is that it is the base to understand the diverse applications. This course focuses on information theory and coding within the context of modern digital communications applications. To let student better understand information theory, we will begin with a directed review of probability. Then we will introduce the basic concepts of information theory, source coding, channel and channel capacity, channel coding. Lossless data compression will be lightly touched in source coding. Channel and channel capacity will include discrete memoryless channel model, Markov processes and source with memory. Channel coding will focus on block error correction code such as hamming codes, BCH, Reed-Solomon coding and so on. Finally, the typical applications of the error correction codes will be introduced.
Introductory probability, Linear algebra and communication system
Written exam (70%) and a mini project with report (30%), 7-scale, external examiner