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Table of contents
Shannon Theory of Communication
From entropy and coding to channel capacity and reliable communication
Read each section in order. Every title can be opened as a TheoryTrace document.
- Cover1
- Copyright2
- How to read this book3
- Introduction4
- Chapter 1: Communication as a Mathematical Problem5
- Chapter 2: Probability for Communication Theory6
- Chapter 3: Information and Self-Information7
- Chapter 4: Entropy of Discrete Sources8
- Chapter 5: Joint, Conditional, and Mutual Information9
- Chapter 6: Relative Entropy and Information Inequalities10
- Chapter 7: Discrete Memoryless Sources11
- Chapter 8: Typical Sequences and the Asymptotic Equipartition Property12
- Chapter 9: Lossless Source Coding13
- Chapter 10: Huffman Coding and Arithmetic Coding14
- Chapter 11: The Source Coding Theorem15
- Chapter 12: Discrete Channels and Transition Probabilities16
- Chapter 13: Channel Capacity17
- Chapter 14: Error Probability, Decision Rules, and Decoding18
- Chapter 15: The Noisy Channel Coding Theorem19
- Chapter 16: Error-Correcting Codes in Shannon Theory20
- Chapter 17: Continuous Channels and Differential Entropy21
- Chapter 18: The Gaussian Channel22
- Chapter 19: Bandwidth, Power, and Spectral Efficiency23
- Chapter 20: Rate-Distortion Theory24
- Chapter 21: Entropy Rates and Sources with Memory25
- Chapter 22: Modern Perspectives and Applications26
- Conclusion27