Information Theory
Informationsteori

EITN45F, 7.5 credits

Valid from: Spring 2018
Decided by: Professor Thomas Johansson
Date of establishment: 2017-09-26

General Information

Division: Electrical and Information Technology
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course code: EITN45
Teaching language: English

Aim

The aim of this course is to give the students knowledge of principles for information storage and transmission of information, and the use of binary representation of information. The course also gives knowledge of the prestanda and fundamental boundaries of todays and tomorrows communication systems.

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• be able to identify and formulate problems within the area of Information Theory
• be able to classify the level of difficulty of problems related to the his/her own level of knowledge

Competences and Skills

For a passing grade the doctoral student must

• be able to show ability to handle new methods and results.
• be able to set up requirements on implementation of algoritms in the course.
• be able to realize systems for the algorithms presented in the course.

Judgement and Approach

For a passing grade the doctoral student must

• be able to classify the level of difficulty of problems related to the his/her own level of knowledge
• be aware of what parameters set up the boundaries for reliable communicationa as well as the compression ratio of a source.

Course Contents

The definition of information goes back to Shannons landmark paper in 1948. His theory of how information can be processed is the basis of all efficient digital communication systems both today and tomorrow. This course provides an up-to-date introduction to topic information theory. The course emphasizes both the formal development of the theory and the engineering implications for the design of communication systems and other information handling systems. The course includes: * Shannon's information measure and its relatives, both for the discrete and continuous case. * Three fundamental information theorems: Typical sequences, Source coding theorem and Channel coding theorem. * Source coding: Optimal coding and construction of Huffman codes, as well as universal souce coding such as Ziv-Lempel coding (zip, etc.). * Channel coding: Principles of error detection and correction on a noisy channel, mainly illustrated by Hamming codes. * Gaussian channel: Continuous sources and additive white noise over both band limited and frequency selective channels, as wellas the multi-dimensional Gauss channel for MIMO systems. Derivation of the fundamental Shanon limit. * Discrete input Gaussian channel: Maximum achievable rates for PAM and QAM, Coding and Shaping gain, and SNR gap.

Course Literature

Höst, S.: Kompendie: Information Theory and Communication Engineering.

Instruction Details

Types of instruction: Lectures, exercises

Examination Details

Examination formats: Written exam, written assignments. The examonations is done thrhrough hand in problems and take home exam.
Grading scale: Failed, pass
Examiner:

Admission Details

Assumed prior knowledge: Knowledge corresponding to a basic course in Probability theory and a course in Digital Communications.

Further Information

Course Coordinator: Stefan Höst, stefan.host@eit.lth.se

Course Occasion Information

Contact and Other Information

Course coordinators:
Web page: http://www.eit.lth.se/kurs/EITN45