《資訊理論與可靠通信》是信息領域諾貝爾獎級別泰斗羅伯特·加拉格爾（Robert G. Gallager）所著的一本資訊理論聖經，一代一代的資訊理論學者都是讀著這本世界經典成長起來的。作者在美國麻省理工學院師從資訊理論創始人克勞德·香農（Claude E. Shannon）及另兩位最早期的香農獎得主羅伯特·法諾（Robert M. Fano）和彼得·埃里亞斯（Peter Elias），博士畢業后一直在麻省理工學院任教至今，被譽為香農以後最偉大的資訊理論學者。他1960年博士論文中提出的「低密度奇偶校驗碼」是目前所有5G設備都必用的通道編碼。《資訊理論與可靠通信》一書中有不少內容是作者當年首次提出的原創性成果，對資訊理論的發展有極大的推動作用。書中深入研究了通信系統中信源和通道的數學模型，並探索了構建真實世界中信源和通道詳細模型的框架。然後，作者通過將編碼器和解碼器分為兩個部分進一步闡述資訊理論原理，並研究構成有效通信系統的機制。本書適合作為電子工程、電腦科學以及數學相關專業的高年級本科生和研究生的資訊理論課程教材，也可供研究人員和專業人士參考。「香農信息科學經典」系列還出版了加拉格爾教授所著的另兩本名著《麻省理工加拉格爾數字通信原理》和《數據網路（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 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
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