Digital Communications 1

Table of contents

1. Cover
2. Title
3. Copyright
4. Preface
5. List of Acronyms
6. Notations
7. Introduction
8. 1: Introduction to Information Theory
   1. 1.1. Introduction
   2. 1.2. Review of probabilities
   3. 1.3. Entropy and mutual information
   4. 1.4. Lossless source coding theorems
   5. 1.5. Theorem for lossy source coding
   6. 1.6. Transmission channel models
   7. 1.7. Capacity of a transmission channel
   8. 1.8. Exercises
9. 2: Source Coding
   1. 2.1. Introduction
   2. 2.2. Algorithms for lossless source coding
   3. 2.3. Sampling and quantization
   4. 2.4. Coding techniques for analog sources with memory
   5. 2.5. Application to the image and sound compression
   6. 2.6. Exercises
10. 3: Linear Block Codes
    1. 3.1. Introduction
    2. 3.2. Finite fields
    3. 3.3. Linear block codes
    4. 3.4. Decoding of binary linear block codes
    5. 3.5. Performances of linear block codes
    6. 3.6. Cyclic codes
    7. 3.7. Applications
    8. 3.8. Exercises
11. 4: Convolutional Codes
    1. 4.1. Introduction
    2. 4.2. Mathematical representations and hardware structures
    3. 4.3. Graphical representation of the convolutional codes
    4. 4.4. Free distance and transfer function of convolutional codes
    5. 4.5. Viterbi’s algorithm for the decoding of convolutional codes
    6. 4.6. Punctured convolutional codes
    7. 4.7. Applications
    8. 4.8. Exercises
12. 5: Concatenated Codes and Iterative Decoding
    1. 5.1. Introduction
    2. 5.2. Soft input soft output decoding
    3. 5.3. LDPC codes
    4. 5.4. Parallel concatenated convolutional codes or turbo codes
    5. 5.5. Other classes of concatenated codes
    6. 5.6. Exercises
13. Appendix A: Proof of the Channel Capacity of the Additive White Gaussian Noise Channel
14. Appendix B: Calculation of the Weight Enumerator Function IRWEF of a Systematic Recursive Convolutional Encoder
15. Bibliography
16. Index
17. End User License Agreement