Course content

Bayesian estimation. Modelling and analysis of components in a generic communication system. Mathematical definitions of information content and channel capacity. Principles for optimal information transfer across various types of channels. Data compression. Principles and methods for practical digital representations. Practical channel coding. Performance assessment relative to information theoretic limits. Belief propagation.

Learning outcome

The course teaches basic knowledge on information-theoretic principles and methods. It enables the students to apply these principles and methods in their research tasks. The general competence gained throughout this course is a prerequisite for TT8101.

Learning methods and activities

Lectures and exercises. Self study.

Recommended previous knowledge

TTT4115 Communications or equivalent.

Course materials

David MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003.
Thomas Cover and Joy Thomas, Elements of Information Theory, 2nd ed. Wiley Interscience, 2006.
Tom Richardson and Rüdiger Urbanke, Modern Coding Theory, Cambridge University Press, 2008.

Credit reductions