《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）及另两位很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抢先发售提出的原创性成果，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）》。

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
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