David G. Luenberger is Professor in the Department of Management Science and Engineering at Stanford University.

Winner of the 2006 Award for Best Professional/Scholarly Book in Computer & Information Science, Association of American Publishers "This is a fascinating and enjoyable book to read. It is clear throughout the book that David Luenberger is an experienced teacher who has put careful thought into his writing. He wrote and uses this book for a course in the Dept. of Engineering--Economic Systems and Operations Research at Stanford University. The students range from sophomores to graduate students, and the book is very readable for students at all of these levels."--Susan Kelly, The UMAP Journal

• Preface xiii Chapter 1: INTRODUCTION 1 1.1 Themes of Analysis 2 1.2 Information Lessons 4 Part I: ENTROPY: The Foundation of Information Chapter 2: INFORMATION DEFINITION 9 2.1 A Measure of Information 10 2.2 The Definition of Entropy 12 2.3 Information Sources 14 2.4 Source Combinations 15 2.5 Bits as a Measure 16 2.6 About Claude E. Shannon 17 2.7 Exercises 18 2.8 Bibliography 19 Chapter 3: CODES 21 3.1 The Coding Problem 21 3.2 Average Code Length and Entropy 27 3.3 Shannon's First Theorem 30 3.4 Exercises 33 3.5 Bibliography 34 Chapter 4: COMPRESSION 35 4.1 Huffman Coding 35 4.2 Intersymbol Dependency 40 4.3 Lempel-Ziv Coding 44 4.4 Other Forms of Compression 48 4.5 Exercises 52 4.6 Bibliography 53 Chapter 5: CHANNELS 55 5.1 Discrete Channel 56 5.2 Conditional and Joint Entropies 57 5.3 Flipping a Channel 60 5.4 Mutual Information 62 5.5 Capacity* 65 5.6 Shannon's Second Theorem* 66 5.7 Exercises 68 5.8 Bibliography 69 Chapter 6: ERROR-CORRECTING CODES 70 6.1 Simple Code Concepts 71 6.2 Hamming Distance 73 6.3 Hamming Codes 75 6.4 Linear Codes 77 6.5 Low-Density Parity Check Codes 78 6.6 Interleaving 79 6.7 Convolutional Codes 80 6.8 Turbo Codes 82 6.9 Applications 83 6.10 Exercises 85 6.11 Bibliography 86 Summary of Part I 89 Part II: ECONOMICS: Strategies for Value Chapter 7: MARKETS 93 7.1 Demand 94 7.2 Producers 97 7.3 Social Surplus 99 7.4 Competition 100 7.5 Optimality of Marginal Cost Pricing 101 7.6 Linear Demand Curves 102 7.7 Copyright and Monopoly 103 7.8 Other Pricing Methods 107 7.9 Oligopoly 108 7.10 Exercises 111 7.11 Bibliography 113 Chapter 8: PRICING SCHEMES 114 8.1 Discrimination 114 8.2 Versions 116 8.3 Bundling 119 8.4 Sharing 124 8.5 Exercises 127 8.6 Bibliography 128 Chapter 9: VALUE 130 9.1 Conditional Information 131 9.2 Informativity and Generalized Entropy* 133 9.3 Decisions 135 9.4 The Structure of Value 135 9.5 Utility Functions* 139 9.6 Informativity and Decision Making* 140 9.7 Exercises 141 9.8 Bibliography 142 Chapter 10: INTERACTION 143 10.1 Common Knowledge 144 10.2 Agree to Disagree? 146 10.3 Information and Decisions 149 10.4 A Formal Analysis* 150 10.5 Metcalfe's Law 153 10.6 Network Economics* 155 10.7 Exercises 159 10.8 Bibliography 160 Summary of Part II 161 Part III: ENCRYPTION: Security through Mathematics Chapter 11: CIPHERS 165 11.1 Definitions 166 11.2 Example Ciphers 166 11.3 Frequency Analysis 169 11.4 Cryptograms 169 11.5 The Vigenere Cipher 171 11.6 The Playfair Cipher 174 11.7 Homophonic Codes 175 11.8 Jefferson's Wheel Cipher 176 11.9 The Enigma Machine 177 11.10 The One-Time Pad 181 11.11 Exercises 183 11.12 Bibliography 184 Chapter 12: CRYPTOGRAPHY THEORY 186 12.1 Perfect Security 186 12.2 Entropy Relations 188 12.3 Use of a One-Time Pad* 193 12.4 The DES and AES Systems 196 12.5 Exercises 197 12.6 Bibliography 198 Chapter 13: PUBLIC KEY CRYPTOGRAPHY 200 13.1 A Basic Dilemma 200 13.2 One-Way Functions 201 13.3 Discrete Logarithms 202 13.4 Diffie-Hellman Key Exchange 203 13.5 Modular Mathematics 205 13.6 Alternative Puzzle Solution 208 13.7 RSA 209 13.8 Square and Multiply* 211 13.9 Finding Primes* 213 13.10 Performance* 214 13.11 The Future 215 Appendix: The Extended Euclidean Algorithm 216 13.12 Exercises 217 13.13 Bibliography 218 Chapter 14: SECURITY PROTOCOLS 220 14.1 Digital Signatures 220 14.2 Blinded Signatures 223 14.3 Digital Cash 225 14.4 Identification 226 14.5 Zero-Knowledge Proofs 228 14.6 Smart Cards 231 14.7 Exercises 234 14.8 Bibliography 235 Summary of Part III 237 Part IV: EXTRACTION: Information from Data Chapter 15: DATA STRUCTURES 241 15.1 Lists 241 15.2 Trees 244 15.3 Traversal of Trees 247 15.4 Binary Search Trees (BST) 248 15.5 Partially Ordered Trees 252 15.6 Tries* 254 15.7 Basic Sorting Algorithms 255 15.8 Quicksort 257 15.9 Heapsort 260 15.10 Merges 261 15.11 Exercises 262 15.12 Bibliography 263 Chapter 16: DATABASE SYSTEMS 264 16.1 Relational Structure 264 16.2 Keys 267 16.3 Operations 267 16.4 Functional Dependencies 271 16.5 Normalization 271 16.6 Joins and Products* 277 16.7 Database Languages 279 16.8 Exercises 281 16.9 Bibliography 282 Chapter 17: INFORMATION RETRIEVAL 284 17.1 Inverted Files 285 17.2 Strategies for Indexing 287 17.3 Inverted File Compression* 291 17.4 Queries 293 17.5 Ranking Methods 294 17.6 Network Rankings 296 17.7 Exercises 299 17.8 Bibliography 299 Chapter 18: DATA MINING 301 18.1 Overview of Techniques 301 18.2 Market Basket Analysis 303 18.3 Least-Squares Approximation 306 18.4 Classification Trees 310 18.5 Bayesian Methods 314 18.6 Support Vector Machines 319 18.7 Other Methods 323 18.8 Exercises 325 18.9 Bibliography 327 Summary of Part IV 327 Part V: EMISSION: The Mastery of Frequency Chapter 19: FREQUENCY CONCEPTS 331 19.1 The Telegraph 334 19.2 When Dots Became Dashes 335 19.3 Fourier Series 338 19.4 The Fourier Transform 339 19.5 Thomas Edison and the Telegraph 342 19.6 Bell and the Telephone 342 19.7 Lessons in Frequency 345 19.8 Exercises 347 19.9 Bibliography 349 Chapter 20: RADIO WAVES 350 20.1 Why Frequencies? 350 20.2 Resonance 354 20.3 The Birth of Radio 354 20.4 Marconi's Radio 355 20.5 The Spark Bandwidth 357 20.6 The Problems 359 20.7 Continuous Wave Generation 360 20.8 The Triode Vacuum Tube 361 20.9 Modulation Mathematics 363 20.10 Heterodyne Principle 365 20.11 Frequency Modulation 367 20.12 Exercises 369 20.13 Bibliography 372 Chapter 21: SAMPLING AND CAPACITY 373 21.1 Entropy 373 21.2 Capacity of the Gaussian Channel 376 21.3 Sampling Theorem 378 21.4 Generalized Sampling Theorem* 380 21.5 Thermal Noise 383 21.6 Capacity of a Band-Limited Channel 384 21.7 Spread Spectrum 385 21.8 Spreading Technique 387 21.9 Multiple Access Systems 388 21.10 Exercises 391 21.11 Bibliography 392 Chapter 22: NETWORKS 393 22.1 Poisson Processes 394 22.2 Frames 395 22.3 The ALOHA System 396 22.4 Carrier Sensing 398 22.5 Routing Algorithms 399 22.6 The Bellman-Ford Algorithm 400 22.7 Distance Vector Routing 401 22.8 Dijkstra's Algorithm 402 22.9 Other Issues 404 22.10 Exercises 405 22.11 Bibliography 406 Summary of Part V 407 Index 409

David G. Luenberger is Professor in the Department of Management Science and Engineering at Stanford University.

Winner of the 2006 Award for Best Professional/Scholarly Book in Computer & Information Science, Association of American Publishers "This is a fascinating and enjoyable book to read. It is clear throughout the book that David Luenberger is an experienced teacher who has put careful thought into his writing. He wrote and uses this book for a course in the Dept. of Engineering--Economic Systems and Operations Research at Stanford University. The students range from sophomores to graduate students, and the book is very readable for students at all of these levels."--Susan Kelly, The UMAP Journal

• Preface xiii Chapter 1: INTRODUCTION 1 1.1 Themes of Analysis 2 1.2 Information Lessons 4 Part I: ENTROPY: The Foundation of Information Chapter 2: INFORMATION DEFINITION 9 2.1 A Measure of Information 10 2.2 The Definition of Entropy 12 2.3 Information Sources 14 2.4 Source Combinations 15 2.5 Bits as a Measure 16 2.6 About Claude E. Shannon 17 2.7 Exercises 18 2.8 Bibliography 19 Chapter 3: CODES 21 3.1 The Coding Problem 21 3.2 Average Code Length and Entropy 27 3.3 Shannon's First Theorem 30 3.4 Exercises 33 3.5 Bibliography 34 Chapter 4: COMPRESSION 35 4.1 Huffman Coding 35 4.2 Intersymbol Dependency 40 4.3 Lempel-Ziv Coding 44 4.4 Other Forms of Compression 48 4.5 Exercises 52 4.6 Bibliography 53 Chapter 5: CHANNELS 55 5.1 Discrete Channel 56 5.2 Conditional and Joint Entropies 57 5.3 Flipping a Channel 60 5.4 Mutual Information 62 5.5 Capacity* 65 5.6 Shannon's Second Theorem* 66 5.7 Exercises 68 5.8 Bibliography 69 Chapter 6: ERROR-CORRECTING CODES 70 6.1 Simple Code Concepts 71 6.2 Hamming Distance 73 6.3 Hamming Codes 75 6.4 Linear Codes 77 6.5 Low-Density Parity Check Codes 78 6.6 Interleaving 79 6.7 Convolutional Codes 80 6.8 Turbo Codes 82 6.9 Applications 83 6.10 Exercises 85 6.11 Bibliography 86 Summary of Part I 89 Part II: ECONOMICS: Strategies for Value Chapter 7: MARKETS 93 7.1 Demand 94 7.2 Producers 97 7.3 Social Surplus 99 7.4 Competition 100 7.5 Optimality of Marginal Cost Pricing 101 7.6 Linear Demand Curves 102 7.7 Copyright and Monopoly 103 7.8 Other Pricing Methods 107 7.9 Oligopoly 108 7.10 Exercises 111 7.11 Bibliography 113 Chapter 8: PRICING SCHEMES 114 8.1 Discrimination 114 8.2 Versions 116 8.3 Bundling 119 8.4 Sharing 124 8.5 Exercises 127 8.6 Bibliography 128 Chapter 9: VALUE 130 9.1 Conditional Information 131 9.2 Informativity and Generalized Entropy* 133 9.3 Decisions 135 9.4 The Structure of Value 135 9.5 Utility Functions* 139 9.6 Informativity and Decision Making* 140 9.7 Exercises 141 9.8 Bibliography 142 Chapter 10: INTERACTION 143 10.1 Common Knowledge 144 10.2 Agree to Disagree? 146 10.3 Information and Decisions 149 10.4 A Formal Analysis* 150 10.5 Metcalfe's Law 153 10.6 Network Economics* 155 10.7 Exercises 159 10.8 Bibliography 160 Summary of Part II 161 Part III: ENCRYPTION: Security through Mathematics Chapter 11: CIPHERS 165 11.1 Definitions 166 11.2 Example Ciphers 166 11.3 Frequency Analysis 169 11.4 Cryptograms 169 11.5 The Vigenere Cipher 171 11.6 The Playfair Cipher 174 11.7 Homophonic Codes 175 11.8 Jefferson's Wheel Cipher 176 11.9 The Enigma Machine 177 11.10 The One-Time Pad 181 11.11 Exercises 183 11.12 Bibliography 184 Chapter 12: CRYPTOGRAPHY THEORY 186 12.1 Perfect Security 186 12.2 Entropy Relations 188 12.3 Use of a One-Time Pad* 193 12.4 The DES and AES Systems 196 12.5 Exercises 197 12.6 Bibliography 198 Chapter 13: PUBLIC KEY CRYPTOGRAPHY 200 13.1 A Basic Dilemma 200 13.2 One-Way Functions 201 13.3 Discrete Logarithms 202 13.4 Diffie-Hellman Key Exchange 203 13.5 Modular Mathematics 205 13.6 Alternative Puzzle Solution 208 13.7 RSA 209 13.8 Square and Multiply* 211 13.9 Finding Primes* 213 13.10 Performance* 214 13.11 The Future 215 Appendix: The Extended Euclidean Algorithm 216 13.12 Exercises 217 13.13 Bibliography 218 Chapter 14: SECURITY PROTOCOLS 220 14.1 Digital Signatures 220 14.2 Blinded Signatures 223 14.3 Digital Cash 225 14.4 Identification 226 14.5 Zero-Knowledge Proofs 228 14.6 Smart Cards 231 14.7 Exercises 234 14.8 Bibliography 235 Summary of Part III 237 Part IV: EXTRACTION: Information from Data Chapter 15: DATA STRUCTURES 241 15.1 Lists 241 15.2 Trees 244 15.3 Traversal of Trees 247 15.4 Binary Search Trees (BST) 248 15.5 Partially Ordered Trees 252 15.6 Tries* 254 15.7 Basic Sorting Algorithms 255 15.8 Quicksort 257 15.9 Heapsort 260 15.10 Merges 261 15.11 Exercises 262 15.12 Bibliography 263 Chapter 16: DATABASE SYSTEMS 264 16.1 Relational Structure 264 16.2 Keys 267 16.3 Operations 267 16.4 Functional Dependencies 271 16.5 Normalization 271 16.6 Joins and Products* 277 16.7 Database Languages 279 16.8 Exercises 281 16.9 Bibliography 282 Chapter 17: INFORMATION RETRIEVAL 284 17.1 Inverted Files 285 17.2 Strategies for Indexing 287 17.3 Inverted File Compression* 291 17.4 Queries 293 17.5 Ranking Methods 294 17.6 Network Rankings 296 17.7 Exercises 299 17.8 Bibliography 299 Chapter 18: DATA MINING 301 18.1 Overview of Techniques 301 18.2 Market Basket Analysis 303 18.3 Least-Squares Approximation 306 18.4 Classification Trees 310 18.5 Bayesian Methods 314 18.6 Support Vector Machines 319 18.7 Other Methods 323 18.8 Exercises 325 18.9 Bibliography 327 Summary of Part IV 327 Part V: EMISSION: The Mastery of Frequency Chapter 19: FREQUENCY CONCEPTS 331 19.1 The Telegraph 334 19.2 When Dots Became Dashes 335 19.3 Fourier Series 338 19.4 The Fourier Transform 339 19.5 Thomas Edison and the Telegraph 342 19.6 Bell and the Telephone 342 19.7 Lessons in Frequency 345 19.8 Exercises 347 19.9 Bibliography 349 Chapter 20: RADIO WAVES 350 20.1 Why Frequencies? 350 20.2 Resonance 354 20.3 The Birth of Radio 354 20.4 Marconi's Radio 355 20.5 The Spark Bandwidth 357 20.6 The Problems 359 20.7 Continuous Wave Generation 360 20.8 The Triode Vacuum Tube 361 20.9 Modulation Mathematics 363 20.10 Heterodyne Principle 365 20.11 Frequency Modulation 367 20.12 Exercises 369 20.13 Bibliography 372 Chapter 21: SAMPLING AND CAPACITY 373 21.1 Entropy 373 21.2 Capacity of the Gaussian Channel 376 21.3 Sampling Theorem 378 21.4 Generalized Sampling Theorem* 380 21.5 Thermal Noise 383 21.6 Capacity of a Band-Limited Channel 384 21.7 Spread Spectrum 385 21.8 Spreading Technique 387 21.9 Multiple Access Systems 388 21.10 Exercises 391 21.11 Bibliography 392 Chapter 22: NETWORKS 393 22.1 Poisson Processes 394 22.2 Frames 395 22.3 The ALOHA System 396 22.4 Carrier Sensing 398 22.5 Routing Algorithms 399 22.6 The Bellman-Ford Algorithm 400 22.7 Distance Vector Routing 401 22.8 Dijkstra's Algorithm 402 22.9 Other Issues 404 22.10 Exercises 405 22.11 Bibliography 406 Summary of Part V 407 Index 409

David G. Luenberger is Professor in the Department of Management Science and Engineering at Stanford University.

Winner of the 2006 Award for Best Professional/Scholarly Book in Computer & Information Science, Association of American Publishers "This is a fascinating and enjoyable book to read. It is clear throughout the book that David Luenberger is an experienced teacher who has put careful thought into his writing. He wrote and uses this book for a course in the Dept. of Engineering--Economic Systems and Operations Research at Stanford University. The students range from sophomores to graduate students, and the book is very readable for students at all of these levels."--Susan Kelly, The UMAP Journal

• Preface xiii Chapter 1: INTRODUCTION 1 1.1 Themes of Analysis 2 1.2 Information Lessons 4 Part I: ENTROPY: The Foundation of Information Chapter 2: INFORMATION DEFINITION 9 2.1 A Measure of Information 10 2.2 The Definition of Entropy 12 2.3 Information Sources 14 2.4 Source Combinations 15 2.5 Bits as a Measure 16 2.6 About Claude E. Shannon 17 2.7 Exercises 18 2.8 Bibliography 19 Chapter 3: CODES 21 3.1 The Coding Problem 21 3.2 Average Code Length and Entropy 27 3.3 Shannon's First Theorem 30 3.4 Exercises 33 3.5 Bibliography 34 Chapter 4: COMPRESSION 35 4.1 Huffman Coding 35 4.2 Intersymbol Dependency 40 4.3 Lempel-Ziv Coding 44 4.4 Other Forms of Compression 48 4.5 Exercises 52 4.6 Bibliography 53 Chapter 5: CHANNELS 55 5.1 Discrete Channel 56 5.2 Conditional and Joint Entropies 57 5.3 Flipping a Channel 60 5.4 Mutual Information 62 5.5 Capacity* 65 5.6 Shannon's Second Theorem* 66 5.7 Exercises 68 5.8 Bibliography 69 Chapter 6: ERROR-CORRECTING CODES 70 6.1 Simple Code Concepts 71 6.2 Hamming Distance 73 6.3 Hamming Codes 75 6.4 Linear Codes 77 6.5 Low-Density Parity Check Codes 78 6.6 Interleaving 79 6.7 Convolutional Codes 80 6.8 Turbo Codes 82 6.9 Applications 83 6.10 Exercises 85 6.11 Bibliography 86 Summary of Part I 89 Part II: ECONOMICS: Strategies for Value Chapter 7: MARKETS 93 7.1 Demand 94 7.2 Producers 97 7.3 Social Surplus 99 7.4 Competition 100 7.5 Optimality of Marginal Cost Pricing 101 7.6 Linear Demand Curves 102 7.7 Copyright and Monopoly 103 7.8 Other Pricing Methods 107 7.9 Oligopoly 108 7.10 Exercises 111 7.11 Bibliography 113 Chapter 8: PRICING SCHEMES 114 8.1 Discrimination 114 8.2 Versions 116 8.3 Bundling 119 8.4 Sharing 124 8.5 Exercises 127 8.6 Bibliography 128 Chapter 9: VALUE 130 9.1 Conditional Information 131 9.2 Informativity and Generalized Entropy* 133 9.3 Decisions 135 9.4 The Structure of Value 135 9.5 Utility Functions* 139 9.6 Informativity and Decision Making* 140 9.7 Exercises 141 9.8 Bibliography 142 Chapter 10: INTERACTION 143 10.1 Common Knowledge 144 10.2 Agree to Disagree? 146 10.3 Information and Decisions 149 10.4 A Formal Analysis* 150 10.5 Metcalfe's Law 153 10.6 Network Economics* 155 10.7 Exercises 159 10.8 Bibliography 160 Summary of Part II 161 Part III: ENCRYPTION: Security through Mathematics Chapter 11: CIPHERS 165 11.1 Definitions 166 11.2 Example Ciphers 166 11.3 Frequency Analysis 169 11.4 Cryptograms 169 11.5 The Vigenere Cipher 171 11.6 The Playfair Cipher 174 11.7 Homophonic Codes 175 11.8 Jefferson's Wheel Cipher 176 11.9 The Enigma Machine 177 11.10 The One-Time Pad 181 11.11 Exercises 183 11.12 Bibliography 184 Chapter 12: CRYPTOGRAPHY THEORY 186 12.1 Perfect Security 186 12.2 Entropy Relations 188 12.3 Use of a One-Time Pad* 193 12.4 The DES and AES Systems 196 12.5 Exercises 197 12.6 Bibliography 198 Chapter 13: PUBLIC KEY CRYPTOGRAPHY 200 13.1 A Basic Dilemma 200 13.2 One-Way Functions 201 13.3 Discrete Logarithms 202 13.4 Diffie-Hellman Key Exchange 203 13.5 Modular Mathematics 205 13.6 Alternative Puzzle Solution 208 13.7 RSA 209 13.8 Square and Multiply* 211 13.9 Finding Primes* 213 13.10 Performance* 214 13.11 The Future 215 Appendix: The Extended Euclidean Algorithm 216 13.12 Exercises 217 13.13 Bibliography 218 Chapter 14: SECURITY PROTOCOLS 220 14.1 Digital Signatures 220 14.2 Blinded Signatures 223 14.3 Digital Cash 225 14.4 Identification 226 14.5 Zero-Knowledge Proofs 228 14.6 Smart Cards 231 14.7 Exercises 234 14.8 Bibliography 235 Summary of Part III 237 Part IV: EXTRACTION: Information from Data Chapter 15: DATA STRUCTURES 241 15.1 Lists 241 15.2 Trees 244 15.3 Traversal of Trees 247 15.4 Binary Search Trees (BST) 248 15.5 Partially Ordered Trees 252 15.6 Tries* 254 15.7 Basic Sorting Algorithms 255 15.8 Quicksort 257 15.9 Heapsort 260 15.10 Merges 261 15.11 Exercises 262 15.12 Bibliography 263 Chapter 16: DATABASE SYSTEMS 264 16.1 Relational Structure 264 16.2 Keys 267 16.3 Operations 267 16.4 Functional Dependencies 271 16.5 Normalization 271 16.6 Joins and Products* 277 16.7 Database Languages 279 16.8 Exercises 281 16.9 Bibliography 282 Chapter 17: INFORMATION RETRIEVAL 284 17.1 Inverted Files 285 17.2 Strategies for Indexing 287 17.3 Inverted File Compression* 291 17.4 Queries 293 17.5 Ranking Methods 294 17.6 Network Rankings 296 17.7 Exercises 299 17.8 Bibliography 299 Chapter 18: DATA MINING 301 18.1 Overview of Techniques 301 18.2 Market Basket Analysis 303 18.3 Least-Squares Approximation 306 18.4 Classification Trees 310 18.5 Bayesian Methods 314 18.6 Support Vector Machines 319 18.7 Other Methods 323 18.8 Exercises 325 18.9 Bibliography 327 Summary of Part IV 327 Part V: EMISSION: The Mastery of Frequency Chapter 19: FREQUENCY CONCEPTS 331 19.1 The Telegraph 334 19.2 When Dots Became Dashes 335 19.3 Fourier Series 338 19.4 The Fourier Transform 339 19.5 Thomas Edison and the Telegraph 342 19.6 Bell and the Telephone 342 19.7 Lessons in Frequency 345 19.8 Exercises 347 19.9 Bibliography 349 Chapter 20: RADIO WAVES 350 20.1 Why Frequencies? 350 20.2 Resonance 354 20.3 The Birth of Radio 354 20.4 Marconi's Radio 355 20.5 The Spark Bandwidth 357 20.6 The Problems 359 20.7 Continuous Wave Generation 360 20.8 The Triode Vacuum Tube 361 20.9 Modulation Mathematics 363 20.10 Heterodyne Principle 365 20.11 Frequency Modulation 367 20.12 Exercises 369 20.13 Bibliography 372 Chapter 21: SAMPLING AND CAPACITY 373 21.1 Entropy 373 21.2 Capacity of the Gaussian Channel 376 21.3 Sampling Theorem 378 21.4 Generalized Sampling Theorem* 380 21.5 Thermal Noise 383 21.6 Capacity of a Band-Limited Channel 384 21.7 Spread Spectrum 385 21.8 Spreading Technique 387 21.9 Multiple Access Systems 388 21.10 Exercises 391 21.11 Bibliography 392 Chapter 22: NETWORKS 393 22.1 Poisson Processes 394 22.2 Frames 395 22.3 The ALOHA System 396 22.4 Carrier Sensing 398 22.5 Routing Algorithms 399 22.6 The Bellman-Ford Algorithm 400 22.7 Distance Vector Routing 401 22.8 Dijkstra's Algorithm 402 22.9 Other Issues 404 22.10 Exercises 405 22.11 Bibliography 406 Summary of Part V 407 Index 409