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Digital Communications 1
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Digital Communications 1
by Mylène Pischella, Didier Le Ruyet
Digital Communications 1
Cover
Title
Copyright
Preface
List of Acronyms
Notations
Introduction
1: Introduction to Information Theory
1.1. Introduction
1.2. Review of probabilities
1.3. Entropy and mutual information
1.4. Lossless source coding theorems
1.5. Theorem for lossy source coding
1.6. Transmission channel models
1.7. Capacity of a transmission channel
1.8. Exercises
2: Source Coding
2.1. Introduction
2.2. Algorithms for lossless source coding
2.3. Sampling and quantization
2.4. Coding techniques for analog sources with memory
2.5. Application to the image and sound compression
2.6. Exercises
3: Linear Block Codes
3.1. Introduction
3.2. Finite fields
3.3. Linear block codes
3.4. Decoding of binary linear block codes
3.5. Performances of linear block codes
3.6. Cyclic codes
3.7. Applications
3.8. Exercises
4: Convolutional Codes
4.1. Introduction
4.2. Mathematical representations and hardware structures
4.3. Graphical representation of the convolutional codes
4.4. Free distance and transfer function of convolutional codes
4.5. Viterbi’s algorithm for the decoding of convolutional codes
4.6. Punctured convolutional codes
4.7. Applications
4.8. Exercises
5: Concatenated Codes and Iterative Decoding
5.1. Introduction
5.2. Soft input soft output decoding
5.3. LDPC codes
5.4. Parallel concatenated convolutional codes or turbo codes
5.5. Other classes of concatenated codes
5.6. Exercises
Appendix A: Proof of the Channel Capacity of the Additive White Gaussian Noise Channel
Appendix B: Calculation of the Weight Enumerator Function IRWEF of a Systematic Recursive Convolutional Encoder
Bibliography
Index
End User License Agreement
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5: Concatenated Codes and Iterative Decoding
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Appendix A: Proof of the Channel Capacity of the Additive White Gaussian Noise Channel
Appendices
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