InfoCoBuild

An Introduction to Information Theory

An Introduction to Information Theory. Instructor: Prof. Adrish Banerjee, Department of Electrical Engineering, IIT Kanpur. Information Theory answers two fundamental questions: what is the maximum data rate at which we can transmit over a communication link, and what is the fundamental limit of data compression. In this course we will explore answers to these two questions. We will study some practical source compression algorithms. We will also study how to compute channel capacity of simple channels. (from nptel.ac.in)

 Lecture 15 - Channel Capacity

In this lecture, we first explain what is a discrete memoryless channel (DMC). We describe two special cases of DMC, namely, uniformly dispersive channel and uniformly focusing channel and give expressions for their channel capacity. We then define a strongly symmetric channel and give expression for its channel capacity. Finally, we show that a special class of DMC called symmetric channel can be decomposed into strongly symmetric channel and hence their channel capacity can be calculated in terms of capacity of decomposed strongly symmetric channel.

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 Lecture 01 - Introduction Lecture 02 - Measure Information Lecture 03 - Information Inequalities Lecture 04 - Problem Solving Session I Lecture 05 - Block to Variable Length Coding: Prefix Free Code Lecture 06 - Block to Variable Length Coding: Bounds on Optimal Code Length Lecture 07 - Block to Variable Length Coding: Huffman Coding Lecture 08 - Variable to Block Length Coding Lecture 09 - The Asymptotic Equipartition Property Lecture 10 - Block to Block Coding of DMS (Discrete Memoryless Source) Lecture 11 - Problem Solving Session II Lecture 12 - Universal Source Coding: Lempel-Ziv Algorithm - LZ77 Lecture 13 - Universal Source Coding: Lempel-Ziv Welch Algorithm (LZW) Lecture 14 - Coding of Sources with Memory Lecture 15 - Channel Capacity Lecture 16 - Jointly Typical Sequences Lecture 17 - Noisy Channel Coding Theorem Lecture 18 - Differential Entropy Lecture 19 - Gaussian Channel Lecture 20 - Parallel Gaussian Channel Lecture 21 - Problem Solving Session III Lecture 22 - Rate Distortion Theory Lecture 23 - Blahut-Arimoto Algorithm Lecture 24 - Problem Solving Session IV