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Unit 1. Introduction: Objective, scope and outcome of the course.
Unit 2. Introduction to information theory: Uncertainty, Information and Entropy, Information measures for continuous random variables, source coding theorem. Discrete Memory less channels, Mutual information,
Conditional entropy.
Unit 3. Source coding schemes for data compaction: Prefix code, Huffman code, Shanon-Fane code & Hempel-Ziv coding channel capacity. Channel coding theorem. Shannon limit.
Unit 4. Linear Block Code: Introduction to error connecting codes, coding & decoding of linear block code, minimum distance consideration, conversion of non-systematic form of matrices into systematic form.
Unit 5. Cyclic Code: Code Algebra, Basic properties of Galois fields (GF) polynomial operations over Galois fields, generating cyclic code by generating polynomial, parity check polynomial. Encoder & decoder for
cyclic codes.
Unit 6. Convolutional Code: Convolutional encoders of different rates. Code Tree, Trllis and state diagram. Maximum likelihood decoding of convolutional code: The viterbi Algorithm fee distance of a convolutional code.
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komal gupta
04 Sep 2013
proper examples are not given
anjali jain
04 Sep 2013
easy to read and learn
Rakesh Pandya
12 Nov 2014
Book is good about theory concepts but it is more improvement about numericals or such as examples because it is more important because in examination it is more given so improve in Examples...