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Information Theory and Coding by J. S. Chitode: A Review
Information theory and coding are two related fields that deal with the representation, transmission, and processing of information. Information theory studies the fundamental limits of communication systems, such as how much information can be transmitted over a noisy channel, or how much data can be compressed without loss. Coding theory develops methods for achieving these limits, such as how to design efficient and reliable codes for error detection and correction.
One of the books that covers these topics in a comprehensive and accessible way is Information Theory and Coding by Dr. J. S. Chitode, a professor of electronics at Bharati Vidyapeeth University College of Engineering, Pune. The book was published by Technical Publications in 2021 and has 534 pages. It is divided into eight chapters that cover the following topics:
- Chapter 1: Introduction to information theory and channel capacity
- Chapter 2: Source coding and entropy
- Chapter 3: Discrete and continuous channels
- Chapter 4: Fundamental limits on performance
- Chapter 5: Linear block codes
- Chapter 6: Cyclic codes
- Chapter 7: Burst error correcting codes
- Chapter 8: Convolutional codes
The book provides a clear and rigorous exposition of the concepts and results of information theory and coding, with numerous examples, exercises, and illustrations. The book also includes appendices on mathematical preliminaries, probability theory, and algebraic structures. The book is suitable for undergraduate and postgraduate students of engineering, as well as researchers and practitioners in the field of communication systems.
The book can be accessed online at Google Books, or downloaded as a PDF file from Scribd. The book is also available in print format from various online and offline retailers.
Some of the main concepts and results that the book covers are:
- Information: A measure of the uncertainty or randomness of a source or a message. Information can be quantified by entropy, which is the average amount of information per symbol in a sequence.
- Source coding: The process of compressing a source output to reduce its redundancy and minimize its length. Source coding can be lossless or lossy, depending on whether the original message can be recovered exactly or approximately from the compressed message.
- Channel: A medium that carries information from a sender to a receiver. A channel can be noisy or noiseless, depending on whether it introduces errors or distortions in the transmitted message.
- Channel coding: The process of adding redundancy to a message to protect it from errors or noise in the channel. Channel coding can be error detecting or error correcting, depending on whether it allows the receiver to detect or correct the errors in the received message.
- Channel capacity: The maximum rate of information that can be reliably transmitted over a channel. Channel capacity depends on the characteristics of the channel and the coding scheme used.
- Coding theorem: A fundamental result that establishes the existence and optimality of certain codes for source coding and channel coding. For example, Shannon's source coding theorem states that there exists a lossless code that can compress any source output to its entropy, and Shannon's channel coding theorem states that there exists an error correcting code that can achieve any rate below the channel capacity.
- Code: A set of rules that defines how to encode and decode messages. A code can be characterized by its parameters, such as its length, size, rate, distance, efficiency, and redundancy.
- Linear block code: A type of code that consists of fixed-length blocks of bits that are linearly related. A linear block code can be represented by a generator matrix or a parity check matrix, and decoded by syndrome decoding or lookup table decoding.
- Cyclic code: A type of linear block code that has the property that any cyclic shift of a codeword is also a codeword. A cyclic code can be generated by a polynomial or a shift register, and decoded by syndrome decoding or polynomial division.
- Burst error correcting code: A type of code that can correct errors that occur in clusters or bursts. A burst error correcting code can be constructed by interleaving, concatenating, or using special codes such as RS codes or Golay codes.
- Convolutional code: A type of code that consists of variable-length blocks of bits that are generated by passing the message bits through a shift register with feedback connections. A convolutional code can be represented by a state diagram, a tree diagram, or a trellis diagram, and decoded by Viterbi algorithm or sequential decoding.