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Information and Communication Theory

Information and Communication Theory

Autorzy
Wydawnictwo Blackwell Science
Data wydania 01/03/2019
Wydanie Pierwsze
Liczba stron 368
Forma publikacji książka w twardej oprawie
Poziom zaawansowania Dla profesjonalistów, specjalistów i badaczy naukowych
Język angielski
ISBN 9781119433781
Kategorie Inżynieria komunikacyjna i telekomunikacyjna
487.00 PLN (z VAT)
$122.45 / €113.21 / £94.94 /
Produkt dostępny
Dostawa 14 dni
Ilość
Do schowka

Opis książki

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.

Information and Communication Theory

Spis treści

Preface ix


Chapter 1 Introduction 1


Chapter 2 Probability Theory 5


2.1 Probabilities 5


2.2 Random Variable 7


2.3 Expectation and Variance 9


2.4 The Law of Large Numbers 17


2.5 Jensen's Inequality 21


2.6 Random Processes 25


2.7 Markov Process 28


Problems 33


Chapter 3 Information Measures 37


3.1 Information 37


3.2 Entropy 41


3.3 Mutual Information 48


3.4 Entropy of Sequences 58


Problems 63


Chapter 4 Optimal Source Coding 69


4.1 Source Coding 69


4.2 Kraft Inequality 71


4.3 Optimal Codeword Length 80


4.4 Huffman Coding 84


4.5 Arithmetic Coding 95


Problems 101


Chapter 5 Adaptive Source Coding 105


5.1 The Problem with Unknown Source Statistics 105


5.2 Adaptive Huffman Coding 106


5.3 The Lempel-Ziv Algorithms 112


5.4 Applications of Source Coding 125


Problems 129


Chapter 6 Asymptotic Equipartition Property and Channel Capacity 133


6.1 Asymptotic Equipartition Property 133


6.2 Source Coding Theorem 138


6.3 Channel Coding 141


6.4 Channel Coding Theorem 144


6.5 Derivation of Channel Capacity for DMC 155


Problems 164


Chapter 7 Channel Coding 169


7.1 Error-Correcting Block Codes 170


7.2 Convolutional Code 188


7.3 Error-Detecting Codes 203


Problems 210


Chapter 8 Information Measures For Continuous Variables 213


8.1 Differential Entropy and Mutual Information 213


8.2 Gaussian Distribution 224


Problems 232


Chapter 9 Gaussian Channel 237


9.1 Gaussian Channel 237


9.2 Parallel Gaussian Channels 244


9.3 Fundamental Shannon Limit 256


Problems 260


Chapter 10 Discrete Input Gaussian Channel 265


10.1 M-PAM Signaling 265


10.2 A Note on Dimensionality 271


10.3 Shaping Gain 276


10.4 SNR Gap 281


Problems 285


Chapter 11 Information Theory and Distortion 289


11.1 Rate-Distortion Function 289


11.2 Limit For Fix Pb 300


11.3 Quantization 302


11.4 Transform Coding 306


Problems 319


Appendix A Probability Distributions 323


A.1 Discrete Distributions 323


A.2 Continuous Distributions 327


Appendix B Sampling Theorem 337


B.1 The Sampling Theorem 337


Bibliography 343


Index 347

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