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Course Detail

Course Name Coding and Information Theory
Course Code 26CY751
Program M. Tech. in Cyber Security
Credits 3
Campus Coimbatore

Syllabus

Syllabus

Information theory- Information, Entropy, Discrete memoryless source, Source coding – Shannon-Fano coding, Huffman coding, Lempel-Ziv and arithmetic codes, Rate distortion theory, Optimum Quantizer Design. Discrete memoryless channel, Mutual information, Channel capacity, Shannon limit, Error control codes – Linear block codes, Error detection and correction, Hamming codes, Reed Muller codes, Golay codes, Cyclic codes, Binary BCH codes, Reed Solomon codes, Decoding algorithms, Trellis representation of codes, Convolution codes and their applications, Viterbi algorithm and decoding. Machine Learning–Assisted Channel Coding, AI-Based Decoding Algorithms

Objectives and Outcomes

Prerequisite

26MA601 Mathematical Foundations for Cyber Security

Course Outcome

Course Outcome(CO) Bloom’s Taxonomy Level
CO 1 Comprehend source coding and channel performance using Information theory L2
CO 2 Apply linear block codes for error detection and correction L3
CO 3 Learn cyclic codes and BCH codes for error detection and correction L4
CO 4 Understand Reed-Solomon codes, convolutional codes, machine learning-based coding, and their decoding L4

CO-PO Mapping

CO-PO Mapping(3-High, 2-Medium, 1-Low)
CO/PO PO 1 PO 2 PO 3 PO 4 PO 5 PO 6 PO 7 PO 8 PO 9 PO 10 PSO1 PSO2 PSO3
CO 1 – 1 1 – – 1 – 2 – – 0 0 0
CO 3 – 1 1 – – 1 – 2 – – 0 0 0
CO 4 – 1 1 – – 1 – 2 – – 1 1 1
CO 5 – 1 1 – – 1 – 2 – – 1 1 1

Text Books / References

  1. R. E. Blahut, Algebraic Codes for Data Transmission, Cambridge University Press, Cambridge, UK, 2003
  2. S. Lin and D.J. Costello, Error Control Coding – Fundamentals and Applications, 2nd Edition, Pearson Education Inc., NJ., USA, 2004.
  3. Elwyn R. Berlekamp, Algebraic Coding Theory: Revised Edition, World Scientific, 2015.
  4. Thomas M. Cover and Joy A. Thomas, Elements of Information Theory, John Wiley & Sons, 2012.
  5. Eldar, Y. C., Goldsmith, A., Gündüz, D., & Poor, H. V. (Eds.). (2022). Machine Learning and Wireless Communications. Cambridge University Press.

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