Lecture Notes in Information Theory

Course Material

Part 1: Fundamentals

Lecture 1: Introduction to Probability and Information Measures

Lecture 2: Information Theory in the communications chain

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Part 2: Source

Lecture 3: Inequalities and Source Coding

Lecture 4: Huffman Coding, SFE codes, Lempel-Ziv codes

Lecture 5: Asymptotic Equipartition Property (AEP)

Lecture 6: AEP and Source Coding theorem

Lecture 7: Markov Sources and Entropy Rate

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Part 3: Communication Channel

Lecture 8: Channel Capacity

Lecture 9: Channel Capacity of Discrete channels: BSC, BEC

Lecture 10: Convolutional channel codes, Turbo Codes, Viterbi decoder

Lecture 11: Channel coding theorem

Lecture 12: Capacity of the Gaussian channel

Lecture 13: Parallel Gaussian Channels and the Waterfilling

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Part 4: Special topics

Lecture 14: Physical Layer security Concepts

Lecture 15: Advanced topics on information theory

Lecture 16: Advanced topics on information theory

Lecture 17: Advanced topics on information theory

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Instructor: Prof. Dr.-Ing. Samah A. M. Ghanem

Assessment Criteria: Midterm 25%, Assignments 25%, Final 50%

Textbook: Elements of Information Theory, by Joy A. Thomas and Thomas M. Cover, Wiley