Digital Communications 1: Source and Channel Codin g

Preface xiii
<p>List of Acronyms xv</p>
<p>Notations xix</p>
<p>Introduction xxiii</p>
<p>Chapter 1 Introduction to Information Theory 1</p>
<p>1.1 Introduction 1</p>
<p>1.2 Review of probabilities 2</p>
<p>1.3 Entropy and mutual information 7</p>
<p>1.4 Lossless source coding theorems 20</p>
<p>1.5 Theorem for lossy source coding 30</p>
<p>1.6 Transmission channel models 34</p>
<p>1.7 Capacity of a transmission channel 40</p>
<p>1.8 Exercises 54</p>
<p>Chapter 2 Source Coding 57</p>
<p>2.1 Introduction 57</p>
<p>2.2 Algorithms for lossless source coding 58</p>
<p>2.3 Sampling and quantization 69</p>
<p>2.4 Coding techniques for analog sources with memory 87</p>
<p>2.5 Application to the image and sound compression 101</p>
<p>2.6 Exercises 116</p>
<p>Chapter 3 Linear Block Codes 121</p>
<p>3.1 Introduction 121</p>
<p>3.2 Finite fields 122</p>
<p>3.3 Linear block codes 127</p>
<p>3.4 Decoding of binary linear block codes 152</p>
<p>3.5 Performance of linear block codes 172</p>
<p>3.6 Cyclic codes 182</p>
<p>3.7 Applications 214</p>
<p>3.8 Exercises 216</p>
<p>Chapter 4 Convolutional Codes 229</p>
<p>4.1 Introduction 229</p>
<p>4.2 Mathematical representations and hardware structures 229</p>
<p>4.3 Graphical representation of the convolutional codes 238</p>
<p>4.4 Free distance and transfer function of convolutional codes 242</p>
<p>4.5 Viterbi′s algorithm for the decoding of convolutional codes 246</p>
<p>4.6 Punctured convolutional codes 249</p>
<p>4.7 Applications 249</p>
<p>4.8 Exercises 250</p>
<p>Chapter 5 Concatenated Codes and Iterative Decoding 253</p>
<p>5.1 Introduction 253</p>
<p>5.2 Soft input soft output decoding 254</p>
<p>5.3 LDPC codes 288</p>
<p>5.4 Parallel concatenated convolutional codes or turbo codes 306</p>
<p>5.5 Other classes of concatenated codes 330</p>
<p>5.6 Exercises 335</p>
<p>Appendices 339</p>
<p>Appendix A 341</p>
<p>Appendix B 343</p>
<p>Bibliography 345</p>
<p>Index 353</p>
<p>Summary of Volume 2 355&nbsp;</p>