Information and Communication Theory

An important text that offers an in-depth guide to how information theory sets the boundaries for data communication

In an accessible and practical style, Information and Communication Theory explores the topic of information theory and includes concrete tools that are appropriate for real-life communication systems.The text investigates the connection between theoretical and practical applications through a wide-variety of topics including an introduction to the basics of probability theory, information, (lossless) source coding, typical sequences as a central concept, channel coding, continuous random variables, Gaussian channels, discrete input continuous channels, and a brief look at rate distortion theory.

The author explains the fundamental theory together with typical compression algorithms and how they are used in reality. He moves on to review source coding and how much a source can be compressed, and also explains algorithms such as the LZ family with applications to e.g. zip or png. In addition to exploring the channel coding theorem, the book includes illustrative examples of codes. This comprehensive text:

• Provides an adaptive version of Huffman coding that estimates source distribution
• Contains a series of problems that enhance an understanding of information presented in the text
• Covers a variety of topics including optimal source coding, channel coding, modulation and much more
• Includes appendices that explore probability distributions and the sampling theorem

Written for graduate and undergraduate students studying information theory, as well as professional engineers, master’s students, Information and Communication Theory offers an introduction to how information theory sets the boundaries for data communication.

Nyckelord:

Guide to Information Theory and Communication Engineering; Understanding Information and Communication Theory; Text on Information and Communication  Theory; Resource to Information and Communication Theory ; Probability Theory; Random variable; Expectation and variance; the law of lagre numbers; Jensen’s inequality; Random Processes; Markov process; Mutual information; Convexity of information measures; entropy of sequences; entropy rate of Markov models; optimal source coding; Kraft inequality and optimal code word length; Huffman codes; Adaptive source coding; the problem of unknown source statistics; Adaptive Huffman coding; Lempel-Ziv Alogrithms; AEP and its consequences; channel coding