Lectures and practical classes provide students with a basic knowledge of information theory and coding
\nin communication systems. Upon completion of the course, students understand the concept of the amount of
\ninformation and discrete sources with and without memory. Students know the theory of statistical
\ncoding, Shannon’s first theorem and procedures for obtaining code with a minimum average codeword
\nlength. Students are familiar with discrete memory and memoryless channels. They understand another
\nShannon’s theorem and protective coding algorithms – block codes, cyclic codes and convolution codes.<\/p>\n
Communication system model. The concept of information. Definition of the amount of information.
\nDiscrete memoryless sources. Properties of entropy. Discrete sources with memory. The entropy of the
\nMarkov source. Continuous sources of information. Statistical coding. Kraft inequality. Prefix code.
\nCompact code. Shannon’s first theorem. Compact code construction methods. Shannon-Fano coding.
\nHuffman coding. Statistical channel model. Discrete memoryless channels. Discrete channel capacity.
\nDiscrete channels with memory. Continuous channels. Continuous channel capacity. Protective
\nencryption. Shannon’s second theorem. Probability of error. Hamming distance. Block codes.
\nConvolutional coding. Viterbi’s algorithm. Basics of trellis coded modulation.<\/p>\n
The entropy of discrete memoryless sources. State diagram, stationary state and symbol
\nprobabilities, trellis diagram and entropy of Markov sources. Code properties – singularity, unambiguous
\ndecodability, prefix code, compact code. Huffman coding, code efficiency and compression ratio. Lempel-
\nZiv 78 algorithm. Discrete memoryless channel, equivocation, irrelevance, mutual information and
\nchannel capacity. Maximum likelihood (ML) and maximum a posteriori (MAP) decision rule. Linear block
\ncodes, Hamming code, cyclic codes, systematic codes and block diagrams of corresponding encoders
\nand decoders. Convolutional codes, code rate, code range, trellis diagram, state diagram, transfer
\nfunction, free code distance, Viterbi’s algorithm. Systematic convolutional encoder.<\/p>\n","protected":false},"excerpt":{"rendered":"
Objectives and outcomes Lectures and practical classes provide students with a basic knowledge of information theory and coding in communication systems. Upon completion of the course, students understand the concept of the amount of information … Read more<\/a><\/p>\n Read more<\/a>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[199],"tags":[],"class_list":["post-13806","post","type-post","status-publish","format-standard","hentry","category-subjects"],"acf":[],"yoast_head":"\n