- 信息论与可靠通信(英文版)
- - 作者：(美)罗伯特·加拉格尔|责编:陈亮//夏丹
  - 出版社：世界图书出版公司
  - ISBN：9787519275945
  - 出版日期：2020/07/01
  - 页数：588
- 售价：59.2

内容大纲
《信息论与可靠通信》是信息领域诺贝尔奖级别泰斗罗伯特·加拉格尔（Robert G. Gallager）所著的一本信息论圣经，一代一代的信息论学者都是读着这本世界经典成长起来的。作者在美国麻省理工学院师从信息论创始人克劳德·香农（Claude E. Shannon）及另两位最早期的香农奖得主罗伯特·法诺（Robert M. Fano）和彼得·埃里亚斯（Peter Elias），博士毕业后一直在麻省理工学院任教至今，被誉为香农以后最伟大的信息论学者。他1960年博士论文中提出的“低密度奇偶校验码”是目前所有5G设备都必用的信道编码。《信息论与可靠通信》一书中有不少内容是作者当年首次提出的原创性成果，对信息论的发展有极大的推动作用。书中深入研究了通信系统中信源和信道的数学模型，并探索了构建真实世界中信源和信道详细模型的框架。然后，作者通过将编码器和解码器分为两个部分进一步阐述信息论原理，并研究构成有效通信系统的机制。本书适合作为电子工程、计算机科学以及数学相关专业的高年级本科生和研究生的信息论课程教材，也可供研究人员和专业人士参考。“香农信息科学经典”系列还出版了加拉格尔教授所著的另两本名著《麻省理工加拉格尔数字通信原理》和《数据网络（第2版）》。
作者介绍
罗伯特·加拉格尔（Robert G.Gallager）教授是美国国家科学院与国家工程院的两院院士。他曾担任国际信息论学会的主席，他于1983年获得信息论的最高奖——香农奖（相当于信息论领域的诺贝尔奖），1990年获得国际电气电子工程师学会最高荣誉奖章（相当于电子工程领域的诺贝尔奖），2003年获得马可尼奖（相当于通信领域的诺贝尔奖），2020年获得日本国际奖（相当于整个应用科学领域的诺贝尔奖）。加拉格尔教授于1960年在美国麻省理工学院获得博士学位后留校任教至今，他1960年博士论文中提出的“低密度奇偶校验码”（LDPC code）是目前所有5G设备都必用的信道编码。他培养出的博士埃尔达尔·阿里坎（Erdal Arikan）提出了5G通信中的另一种重要信道编码“极化码”（Polar code）。
目录
1  Communication Systems and Information Theory
  1.1  Introduction
  1.2  Source Models and Source Coding
  1.3  Channel Models and Channel Coding
  Historical Notes and References
2  AMeasure of Information
  2.1  Discrete Probability:Review and Notation
  2.2  Definition of Mutual Information
  2.3  Average Mutual Information and Entropy
  2.4  Probability and MutualInformation for Continuous Ensembles
  2.5  Mutual Information for Arbitrary Ensembles
  Summary and Conclusions
  Historical Notes and References
3  Coding for Discrete Sources
  3.1  Fixed-Length Codes
  3.2  Variable-Length Code Words
  3.3  A Source Coding Theorem
  3.4  An Optimum Variable-Length Encoding Procedure
  3.5  Discrete Stationary Sources
  3.6  Markov Sources
  Summary and Conclusions
  Historical Notes and References
4  Discrete Memoryless Channels and Capacity
  4.1  Classification of Channels
  4.2  Discrete Memoryless Channels
  4.3  The Converse to the Coding Theorem
  4.4  Convex Functions
  4.5  Finding Channel Capacity for a Discrete Memoryless Channel
  4.6  Discrete Channels with Memory
    Indecomposable Channels
  Summary and Conclusions
  Historical Notes and References
  Appendix 4A
5  The Noisy-Channel Couing Theorem
  5.1  Block Codes
  5.2  Decoding Block Codes
  5.3  Error Probability for Two Code Words
  5.4  The Generalized Chebyshev Inequality and the Chermor Bound
  5.5  Randomly Chosen Code Words
  5.6  Many Code Words-The Coding Theorem
    Properties of the Random Coding Exponent
  5.7  Eror Probability for an Expurgated Ensemble of Codes
  5.8  Lower Bounds to Error Probability
    Block Error Probability at Rates above Capacity
  5.9  The Coding Theorem for Finite-State Channels
    State Known at Receiver
  Summary and Conclusions
  Historical Notes and References
  Appendix 5A
  Appendix 5B

6  Techniques for Coding and Decoding
  6.1  Parity-Check Codes
    Generator Matrices
    Parity-Check Matrices for Systematic Parity-Check Codes
    Decoding Tables
    Hamming Codes
  6.2  The Coding Theorem for Parity-Check Codes
  6.3  Group Theory
    Subgroups
    Cyclic Subgroups
  6.4  Fields and Polynomials
    Polynomials
  6.5  Cyclic Codes
  6.6  Galois Fields
    Maximal Length Codes and Hamming Codes
    Existence of Galois Fields
  6.7  BCH Codes
    Iterative Algorithm for Finding o(D)
  6.8  Convolutional Codes and Threshold Decoding
  6.9  Sequential Decoding
    Computation for Sequential Decoding
    Error Probability for Sequential Decoding
  6.10  Coding for Burst Noise Channels
    Cyclic Codes
    Convolutional Codes
  Summary and Conclusions
  Historical Notes and References
  Appendix 6A
  Appendix 6B
7  Memoryless Channels with Discrete Time
  7.1  Introduction
  7.2  Unconstrained Inputs
  7.3  Constrained Inputs
  7.4  Additive Noise and Additive Gaussian Noise
    Additive Gaussian Noise with an Energy Constrained Input
  7.5  Parallel Additive Gaussian Noise Channels
  Summary and Conclusions
  Historical Notes and References
8  Waveform Channels
  8.1  Orthonormal Expansions of Signals and White Gaussian Noise
    Gaussian Random Processes
    Mutual Information for Continuous-Time Channels
  8.2  White Gaussian Noise and Orthogonal Signals
    Error Probability for Two Code Words
    Error Probability for Orthogonal Code Words
  8.3  Heuristic Treatment of Capacity for Channels with Additive
    Gaussian Noise and Bandwidth Constraints
  8.4  Representation of Linear Filters and Nonwhite Noise
    Filtered Noise and the Karhunen-Loeve Expansion
    Low-Pass Ideal Filters

  8.5  Additive Gaussian Noise Channels with an Input Constraine in Power and Frequency
  8.6  Fading Dispersive Channels
  Summary and Conclusions
  Historical Notes and References
9  Source Coding with a Fidelity Criterion
  9.1  Introduction
  9.2  Discrete Memoryless Sources and Single-Leer Distorton Measures
  3.3  The Coding Theorem for Sources with a Fidelity Criterior
  9.4  Calculation of R(d*)
  9.5  The Converse to the Noisy-Channel Coding Theorem Revisited
  9.6  Discrete-Time Sources with Continuous Amplitudes
  9.7  Gausian Sources with Square Difference Distortion
    Gaussian Random-Process Sources
  9.8  Discrete Ergodic Sources
  Summary and Conclusions
  Historical Notes and References
  Exercises and Problems
  References and Selected Reading
  Glossary of Symbols
Index

内容大纲

作者介绍

目录

同类热销排行榜

推荐书目