Table of Contents -- 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 -- Appendices -- 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 -- Summary of Volume 2: Digital Communications 2 -- End User License Agreement.