EE410 Information Theory - Maynooth University

Module Name Module Code Version Last Reviewed

Information Theory EE410 2012–2013b 18th Jan. 2013

Module Co-ordinator Department

Refer to Excel document Module_Co-ordinators Electronic Engineering

Module Level Credit rating

4 5 ECTS Credits

Pre-requisites Co-requisites

EE304 Probability and Statistics; EE306 Modulation and Coding Techniques None


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Learning Outcomes

At the end of this module, the student will be able to: 1. Perform source coding on a stationary data source with known statistics using Huffman coding, Shannon coding, arithmetic coding. 2. Calculate the entropy (self, mutual, conditional) of a random variable and interpret the result in terms of information content and theoretical limits to source coding performance. 3. Perform data compression of a stationary data source with unknown statistics. 4. Calculate the channel capacity of simple noisy channels including the binary symmetric case, and interpret the result in terms of theoretical limits to channel coding performance. 5. Design linear block codes (including Hamming and cyclic) and determine the error correcting capability of such codes. 6. Implement syndrome decoding for block codes. 7. Implement simple convolutional codes using state transition and trellis diagrams.

To give an introduction to source and channel coding. To develop the basic information theory to underpin these topics.

Time Allowance for Constituent Elements Lectures Tutorials Assignments (2 x 5hr) Quiz Independent study Semester Examination

24 hours 10 hours 10 hours 2 hours 77 hours 2 hours

Indicative Syllabus   

Introduction – The meaning of Information Theory, Information, Uncertainty and Random variables, Sources of Information, Information Transfer, Mathematical Description of Information Information Sources – The discrete source, discrete source types, modelling the continuous source, capacity of source types Source Coding – The Kraft Inequality, McMillan Inequality, Shannon-Fano-Elias coding,Huffman coding, Arithmetic coding, Lempel-Ziv compression

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Entropy –Self Information, Mutual Information, Entropy rate, Markov processes Noisy channels, channel capacity, error probability and the Channel coding theorem Introduction to Channel Coding – Noisy channels, mutual information and channel capacity, error probability, information rate of a code. Shannon’s Channel Coding Theorem Introduction to Block Codes – Hamming distance, minimum distance decoding, the Packing bound, Gilbert-Varshamov bound. Linear Block Codes – generator matrix, parity check matrix, constructing 1-error correcting codes, syndrome decoding, perfect codes and Hamming codes Cyclic codes – generator and parity check polynomial, generator and parity matrices in systematic form, multiple error-correction and BCH codes Introduction to convolutional codes – state transition diagrams and trellis diagrams.

Assessment Criteria Semester Examination Assignments (2) Quiz

70% 20% 10%

Penalties: Late submission of assignments will be subject to a penalty of 10% of the assessment grade for each day (or part thereof) overdue. Pass Standard and any Special Requirements for Passing Modules: Pass 40% - students are not required to pass the written and continuous components separately - an overall pass mark of 40% is acceptable. Requirements for Autumn Supplemental Examination: The continuous assessment mark is carried forward to the Autumn examinations as there is no facility available for repeating the continuous assessment components of the course. Continual Assessment Results: Assignments will be corrected within two weeks, where that does not extend past the end of the semester. Results and corrected assignments will be available for viewing upon request. Assessment Philosophy Information Theory is a fundamental subject in communications and computer engineering. It requires a strong understanding of probabilistic and algebraic concepts and their applications in the setting of data compression and transmission. Weekly tutorials/labs are designed to reinforce material presented in lectures and will provide an informal assessment of the student’s grasp of all learning outcomes as the module progresses. There are two formal assignments given throughout the semester; these require the student to read around the core material of the lectures and will explicitly test learning outcomes 1-2 (Assignment 1) and outcomes 3-5 (Assignment 2). The class quiz assesses all learning outcomes with particular emphasis on outcomes 5-7. The final examination assesses all outcomes. Course Text

N. L. Biggs, Codes: An Introduction to Information Communication and Cryptography, Springer, 2008


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Simon Haykin, Communication Systems (4th ed.), Wiley, 2000 Thomas M. Cover and Joy A. Thomas, Elements of Information Theory, 2nd Edition, Wiley, 2006 R.J. McEliece, The Theory of Information and Coding, 2ndEdition, Cambridge University Press, 2002

Programmes currently utilising module BE in Electronic Engineering with Computers BE in Electronic Engineering with Communications


EE410 Information Theory - Maynooth University

Module Name Module Code Version Last Reviewed Information Theory EE410 2012–2013b 18th Jan. 2013 Module Co-ordinator Department Refer to Excel docu...

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