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Principles of Communications, 7th Edition

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Principles of Communications, 7th Edition

Rodger E. Ziemer, William H. Tranter

ISBN: 978-1-118-80452-0 January 2014 760 Pages

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Ziemer and Tranter provide a thorough treatment of the principles of communications at the physical layer suitable for college seniors, beginning graduate students, and practicing engineers. This is accomplished by providing overviews of the necessary background in signal, system, probability, and random process theory required for the analog and digital communications topics covered in the book. In addition to stressing fundamental concepts, the seventh edition features sections on important areas such as spread spectrum, cellular communications, and orthogonal frequency-division multiplexing. While the book is aimed at a two-semester course, more than enough material is provided for structuring courses according to students need and instructor preference.

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CHAPTER 1 INTRODUCTION 1

1.1 The Block Diagram of a Communication System 4

1.2 Channel Characteristics 5

1.2.1 Noise Sources 5

1.2.2 Types of Transmission Channels 7

1.3 Summary of Systems-Analysis Techniques 13

1.3.1 Time and Frequency-Domain Analyses 13

1.3.2 Modulation and Communication Theories 13

1.4 Probabilistic Approaches to System Optimization 14

1.4.1 Statistical Signal Detection and EstimationTheory 14

1.4.2 Information Theory and Coding 15

1.4.3 Recent Advances 16

1.5 Preview of This Book 16

Further Reading 16

CHAPTER 2 SIGNAL AND LINEAR SYSTEM ANALYSIS 17

2.1 Signal Models 17

2.1.1 Deterministic and Random Signals 17

2.1.2 Periodic and Aperiodic Signals 18

2.1.3 Phasor Signals and Spectra 18

2.1.4 Singularity Functions 21

2.2 Signal Classifications 24

2.3 Fourier Series 26

2.3.1 Complex Exponential Fourier Series 26

2.3.2 Symmetry Properties of the Fourier Coefficients 27

2.3.3 Trigonometric Form of the Fourier Series 28

2.3.4 Parseval’s Theorem 28

2.3.5 Examples of Fourier Series 29

2.3.6 Line Spectra 30

2.4 The Fourier Transform 34

2.4.1 Amplitude and Phase Spectra 35

2.4.2 Symmetry Properties 36

2.4.3 Energy Spectral Density 37

2.4.4 Convolution 38

2.4.5 Transform Theorems: Proofs and Applications 40

2.4.6 Fourier Transforms of Periodic Signals 48

2.4.7 Poisson Sum Formula 50

2.5 Power Spectral Density and Correlation 50

2.5.1 The Time-Average Autocorrelation Function 51

2.5.2 Properties of 𝑅(𝜏) 52

2.6 Signals and Linear Systems 55

2.6.1 Definition of a Linear Time-Invariant System 56

2.6.2 Impulse Response and the SuperpositionIntegral 56

2.6.3 Stability 58

2.6.4 Transfer (Frequency Response) Function 58

2.6.5 Causality 58

2.6.6 Symmetry Properties of 𝐻(𝑓) 59

2.6.7 Input-Output Relationships for Spectral Densities 62

2.6.8 Response to Periodic Inputs 62

2.6.9 Distortionless Transmission 64

2.6.10 Group and Phase Delay 64

2.6.11 Nonlinear Distortion 67

2.6.12 Ideal Filters 68

2.6.13 Approximation of Ideal Lowpass Filters by Realizable Filters 70

2.6.14 Relationship of Pulse Resolution and Risetime to Bandwidth 75

2.7 Sampling Theory 78

2.8 The Hilbert Transform 82

2.8.1 Definition 82

2.8.2 Properties 83

2.8.3 Analytic Signals 85

2.8.4 Complex Envelope Representation of Bandpass Signals 87

2.8.5 Complex Envelope Representation of Bandpass Systems 89

2.9 The Discrete Fourier Transform and Fast Fourier Transform 91

Further Reading 95

Summary 95

Drill Problems 98

Problems 100

Computer Exercises 111

CHAPTER 3 LINEAR MODULATION TECHNIQUES 112

3.1 Double-Sideband Modulation 113

3.2 Amplitude Modulation (AM) 116

3.2.1 Envelope Detection 118

3.2.2 The Modulation Trapezoid 122

3.3 Single-Sideband (SSB) Modulation 124

3.4 Vestigial-Sideband (VSB) Modulation 133

3.5 Frequency Translation and Mixing 136

3.6 Interference in Linear Modulation 139

3.7 Pulse Amplitude Modulation---PAM 142

3.8 Digital Pulse Modulation 144

3.8.1 Delta Modulation 144

3.8.2 Pulse-Code Modulation 146

3.8.3 Time-Division Multiplexing 147

3.8.4 An Example: The Digital Telephone System 149

Further Reading 150

Summary 150

Drill Problems 151

Problems 152

Computer Exercises 155

CHAPTER 4 ANGLE MODULATION ANDMULTIPLEXING 156

4.1 Phase and Frequency Modulation Defined 156

4.1.1 Narrowband Angle Modulation 157

4.1.2 Spectrum of an Angle-Modulated Signal 161

4.1.3 Power in an Angle-Modulated Signal 168

4.1.4 Bandwidth of Angle-Modulated Signals 168

4.1.5 Narrowband-to-Wideband Conversion 173

4.2 Demodulation of Angle-Modulated Signals 175

4.3 Feedback Demodulators: The Phase-Locked Loop 181

4.3.1 Phase-Locked Loops for FM and PM Demodulation 181

4.3.2 Phase-Locked Loop Operation in the Tracking Mode: The Linear Model 184

4.3.3 Phase-Locked Loop Operation in the Acquisition Mode 189

4.3.4 Costas PLLs 194

4.3.5 Frequency Multiplication and Frequency Division 195

4.4 Interference in Angle Modulation 196

4.5 Analog Pulse Modulation 201

4.5.1 Pulse-Width Modulation (PWM) 201

4.5.2 Pulse-Position Modulation (PPM) 203

4.6 Multiplexing 204

4.6.1 Frequency-Division Multiplexing 204

4.6.2 Example of FDM: Stereophonic FM Broadcasting 205

4.6.3 Quadrature Multiplexing 206

4.6.4 Comparison of Multiplexing Schemes 207

Further Reading 208

Summary 208

Drill Problems 209

Problems 210

Computer Exercises 213

CHAPTER 5 PRINCIPLES OF BASEBAND DIGITAL DATATRANSMISSION 215

5.1 Baseband Digital Data Transmission Systems 215

5.2 Line Codes and Their Power Spectra 216

5.2.1 Description of Line Codes 216

5.2.2 Power Spectra for Line-Coded Data 218

5.3 Effects of Filtering of Digital Data---ISI 225

5.4 Pulse Shaping: Nyquist’s Criterion for Zero ISI 227

5.4.1 Pulses Having the Zero ISI Property 228

5.4.2 Nyquist’s Pulse-Shaping Criterion 229

5.4.3 Transmitter and Receiver Filters for Zero ISI 231

5.5 Zero-Forcing Equalization 233

5.6 Eye Diagrams 237

5.7 Synchronization 239

5.8 Carrier Modulation of Baseband Digital Signals 243

Further Reading 244

Summary 244

Drill Problems 245

Problems 246

Computer Exercises 249

CHAPTER 6 OVERVIEW OF PROBABILITY AND RANDOMVARIABLES 250

6.1 What is Probability? 250

6.1.1 Equally Likely Outcomes 250

6.1.2 Relative Frequency 251

6.1.3 Sample Spaces and the Axioms of Probability 252

6.1.4 Venn Diagrams 253

6.1.5 Some Useful Probability Relationships 253

6.1.6 Tree Diagrams 257

6.1.7 Some More General Relationships 259

6.2 Random Variables and Related Functions 260

6.2.1 Random Variables 260

6.2.2 Probability (Cumulative) Distribution Functions 262

6.2.3 Probability-Density Function 263

6.2.4 Joint cdfs and pdfs 265

6.2.5 Transformation of Random Variables 270

6.3 Statistical Averages 274

6.3.1 Average of a Discrete Random Variable 274

6.3.2 Average of a Continuous Random Variable 275

6.3.3 Average of a Function of a Random Variable 275

6.3.4 Average of a Function of More Than One Random Variable 277

6.3.5 Variance of a Random Variable 279

6.3.6 Average of a Linear Combination of 𝑁Random Variables 280

6.3.7 Variance of a Linear Combination of Independent Random Variables 281

6.3.8 Another Special Average---The Characteristic Function 282

6.3.9 The pdf of the Sum of Two Independent Random Variables 283

6.3.10 Covariance and the Correlation Coefficient 285

6.4 Some Useful pdfs 286

6.4.1 Binomial Distribution 286

6.4.2 Laplace Approximation to the Binomial Distribution 288

6.4.3 Poisson Distribution and Poisson Approximation to the Binomial Distribution 289

6.4.4 Geometric Distribution 290

6.4.5 Gaussian Distribution 291

6.4.6 Gaussian 𝑄-Function 295

6.4.7 Chebyshev’s Inequality 296

6.4.8 Collection of Probability Functions and Their Means and Variances 296

Further Reading 298

Summary 298

Drill Problems 300

Problems 301

Computer Exercises 307

CHAPTER 7 RANDOM SIGNALS AND NOISE 308

7.1 A Relative-Frequency Description of Random Processes 308

7.2 Some Terminology of Random Processes 310

7.2.1 Sample Functions and Ensembles 310

7.2.2 Description of Random Processes in Terms of Joint pdfs 311

7.2.3 Stationarity 311

7.2.4 Partial Description of Random Processes: Ergodicity 312

7.2.5 Meanings of Various Averages for Ergodic Processes 315

7.3 Correlation and Power Spectral Density 316

7.3.1 Power Spectral Density 316

7.3.2 The Wiener--Khinchine Theorem 318

7.3.3 Properties of the Autocorrelation Function 320

7.3.4 Autocorrelation Functions for Random Pulse Trains 321

7.3.5 Cross-Correlation Function and Cross-Power Spectral Density 324

7.4 Linear Systems and Random Processes 325

7.4.1 Input-Output Relationships 325

7.4.2 Filtered Gaussian Processes 327

7.4.3 Noise-Equivalent Bandwidth 329

7.5 Narrowband Noise 333

7.5.1 Quadrature-Component and Envelope-Phase Representation 333

7.5.2 The Power Spectral Density Function of 𝑛𝑐(𝑡) and𝑛𝑠(𝑡) 335

7.5.3 Ricean Probability Density Function 338

Further Reading 340

Summary 340

Drill Problems 341

Problems 342

Computer Exercises 348

CHAPTER 8 NOISE IN MODULATION SYSTEMS 349

8.1 Signal-to-Noise Ratios 350

8.1.1 Baseband Systems 350

8.1.2 Double-Sideband Systems 351

8.1.3 Single-Sideband Systems 353

8.1.4 Amplitude Modulation Systems 355

8.1.5 An Estimator for Signal-to-Noise Ratios 361

8.2 Noise and Phase Errors in Coherent Systems 366

8.3 Noise in Angle Modulation 370

8.3.1 The Effect of Noise on the Receiver Input 370

8.3.2 Demodulation of PM 371

8.3.3 Demodulation of FM: Above Threshold Operation 372

8.3.4 Performance Enhancement through the Use ofDe-emphasis 374

8.4 Threshold Effect in FM Demodulation 376

8.4.1 Threshold Effects in FM Demodulators 376

8.5 Noise in Pulse-Code Modulation 384

8.5.1 Postdetection SNR 384

8.5.2 Companding 387

Further Reading 389

Summary 389

Drill Problems 391

Problems 391

Computer Exercises 394

CHAPTER 9 PRINCIPLES OF DIGITAL DATA TRANSMISSIONIN NOISE 396

9.1 Baseband Data Transmission in White Gaussian Noise 398

9.2 Binary Synchronous Data Transmission with Arbitrary Signal Shapes 404

9.2.1 Receiver Structure and Error Probability 404

9.2.2 The Matched Filter 407

9.2.3 Error Probability for the Matched-Filter Receiver 410

9.2.4 Correlator Implementation of the Matched-Filter Receiver 413

9.2.5 Optimum Threshold 414

9.2.6 Nonwhite (Colored) Noise Backgrounds 414

9.2.7 Receiver Implementation Imperfections 415

9.2.8 Error Probabilities for Coherent Binary Signaling 415

9.3 Modulation Schemes not Requiring Coherent References 421

9.3.1 Differential Phase-Shift Keying (DPSK) 422

9.3.2 Differential Encoding and Decoding of Data 427

9.3.3 Noncoherent FSK 429

9.4 M-ary Pulse-Amplitude Modulation (PAM) 431

9.5 Comparison of Digital Modulation Systems 435

9.6 Noise Performance of Zero-ISI Digital Data Transmission Systems 438

9.7 Multipath Interference 443

9.8 Fading Channels 449

9.8.1 Basic Channel Models 449

9.8.2 Flat-Fading Channel Statistics and Error Probabilities 450

9.9 Equalization 455

9.9.1 Equalization by Zero-Forcing 455

9.9.2 Equalization by MMSE 459

9.9.3 Tap Weight Adjustment 463

Further Reading 466

Summary 466

Drill Problems 468

Problems 469

Computer Exercises 476

CHAPTER 10 ADVANCED DATA COMMUNICATIONSTOPICS 477

10.1 M-ary Data Communications Systems 477

10.1.1 M-ary Schemes Based on Quadrature Multiplexing 477

10.1.2 OQPSK Systems 481

10.1.3 MSK Systems 482

10.1.4 M-ary Data Transmission in Terms of Signal Space 489

10.1.5 QPSK in Terms of Signal Space 491

10.1.6 M-ary Phase-Shift Keying 493

10.1.7 Quadrature-Amplitude Modulation (QAM) 495

10.1.8 Coherent FSK 497

10.1.9 Noncoherent FSK 498

10.1.10 Differentially Coherent Phase-Shift Keying 502

10.1.11 Bit Error Probability from Symbol Error Probability 503

10.1.12 Comparison of M-ary Communications Systems on the Basis of Bit Error Probability 505

10.1.13 Comparison of M-ary Communications Systems on the Basis of Bandwidth Efficiency 508

10.2 Power Spectra for Digital Modulation 510

10.2.1 Quadrature Modulation Techniques 510

10.2.2 FSK Modulation 514

10.2.3 Summary 516

10.3 Synchronization 516

10.3.1 Carrier Synchronization 517

10.3.2 Symbol Synchronization 520

10.3.3 Word Synchronization 521

10.3.4 Pseudo-Noise (PN) Sequences 524

10.4 Spread-Spectrum Communication Systems 528

10.4.1 Direct-Sequence Spread Spectrum 530

10.4.2 Performance of DSSS in CW Interference Environments 532

10.4.3 Performance of Spread Spectrum in Multiple User Environments 533

10.4.4 Frequency-Hop Spread Spectrum 536

10.4.5 Code Synchronization 537

10.4.6 Conclusion 539

10.5 Multicarrier Modulation and Orthogonal Frequency-Division Multiplexing 540

10.6 Cellular Radio Communication Systems 545

10.6.1 Basic Principles of Cellular Radio 546

10.6.2 Channel Perturbations in Cellular Radio 550

10.6.3 Multiple-Input Multiple-Output (MIMO) Systems---Protection Against Fading 551

10.6.4 Characteristics of 1G and 2G Cellular Systems 553

10.6.5 Characteristics of cdma2000 and W-CDMA 553

10.6.6 Migration to 4G 555

Further Reading 556

Summary 556

Drill Problems 557

Problems 558

Computer Exercises 563

CHAPTER 11 OPTIMUM RECEIVERS AND SIGNAL-SPACECONCEPTS 564

11.1 Bayes Optimization 564

11.1.1 Signal Detection versus Estimation 564

11.1.2 Optimization Criteria 565

11.1.3 Bayes Detectors 565

11.1.4 Performance of Bayes Detectors 569

11.1.5 The Neyman-Pearson Detector 572

11.1.6 Minimum Probability of Error Detectors 573

11.1.7 The Maximum a Posteriori (MAP) Detector 573

11.1.8 Minimax Detectors 573

11.1.9 The M-ary Hypothesis Case 573

11.1.10 Decisions Based on Vector Observations 574

11.2 Vector Space Representation of Signals 574

11.2.1 Structure of Signal Space 575

11.2.2 Scalar Product 575

11.2.3 Norm 576

11.2.4 Schwarz’s Inequality 576

11.2.5 Scalar Product of Two Signals in Terms of Fourier Coefficients 578

11.2.6 Choice of Basis Function Sets---The Gram--Schmidt Procedure 579

11.2.7 Signal Dimensionality as a Function of Signal Duration 581

11.3 Map Receiver for Digital Data Transmission 583

11.3.1 Decision Criteria for Coherent Systems in Terms of Signal Space 583

11.3.2 Sufficient Statistics 589

11.3.3 Detection of𝑀-ary Orthogonal Signals 590

11.3.4 A Noncoherent Case 592

11.4 Estimation Theory 596

11.4.1 Bayes Estimation 596

11.4.2 Maximum-Likelihood Estimation 598

11.4.3 Estimates Based onMultiple Observations 599

11.4.4 Other Properties of ML Estimates 601

11.4.5 Asymptotic Qualities of ML Estimates 602

11.5 Applications of Estimation Theory to Communications 602

11.5.1 Pulse-Amplitude Modulation (PAM) 603

11.5.2 Estimation of Signal Phase: The PLL Revisited 604

Further Reading 606

Summary 607

Drill Problems 607

Problems 608

Computer Exercises 614

CHAPTER 12 INFORMATION THEORY AND CODING 615

12.1 Basic Concepts 616

12.1.1 Information 616

12.1.2 Entropy 617

12.1.3 Discrete Channel Models 618

12.1.4 Joint and Conditional Entropy 621

12.1.5 Channel Capacity 622

12.2 Source Coding 626

12.2.1 An Example of Source Coding 627

12.2.2 Several Definitions 630

12.2.3 Entropy of an Extended Binary Source 631

12.2.4 Shannon--Fano Source Coding 632

12.2.5 Huffman Source Coding 632

12.3 Communication in Noisy Environments: Basic Ideas 634

12.4 Communication in Noisy Channels: Block Codes 636

12.4.1 Hamming Distances and Error Correction 637

12.4.2 Single-Parity-Check Codes 638

12.4.3 Repetition Codes 639

12.4.4 Parity-Check Codes for Single Error Correction 640

12.4.5 Hamming Codes 644

12.4.6 Cyclic Codes 645

12.4.7 The Golay Code 647

12.4.8 Bose--Chaudhuri--Hocquenghem (BCH) Codes and Reed Solomon Codes 648

12.4.9 Performance Comparison Techniques 648

12.4.10 Block Code Examples 650

12.5 Communication in Noisy Channels: Convolutional Codes 657

12.5.1 Tree and Trellis Diagrams 659

12.5.2 The Viterbi Algorithm 661

12.5.3 Performance Comparisons for Convolutional Codes 664

12.6 Bandwidth and Power Efficient Modulation (TCM) 668

12.7 Feedback Channels 672

12.8 Modulation and Bandwidth Efficiency 676

12.8.1 Bandwidth and SNR 677

12.8.2 Comparison of Modulation Systems 678

12.9 Quick Overviews 679

12.9.1 Interleaving and Burst-Error Correction 679

12.9.2 Turbo Coding 681

12.9.3 Source Coding Examples 683

12.9.4 Digital Television 685

Further Reading 686

Summary 686

Drill Problems 688

Problems 688

Computer Exercises 692

APPENDIX A PHYSICAL NOISE SOURCES 693

A.1 Physical Noise Sources 693

A.1.1 Thermal Noise 693

A.1.2 Nyquist’s Formula 695

A.1.3 Shot Noise 695

A.1.4 Other Noise Sources 696

A.1.5 Available Power 696

A.1.6 Frequency Dependence 697

A.1.7 Quantum Noise 697

A.2 Characterization of Noise in Systems 698

A.2.1 Noise Figure of a System 699

A.2.2 Measurement of Noise Figure 700

A.2.3 Noise Temperature 701

A.2.4 Effective Noise Temperature 702

A.2.5 Cascade of Subsystems 702

A.2.6 Attenuator Noise Temperature and Noise Figure 704

A.3 Free-Space Propagation Example 705

Further Reading 708

Problems 708

APPENDIX B JOINTLY GAUSSIAN RANDOM VARIABLES 710

B.1 The pdf 710

B.2 The Characteristic Function 711

B.3 Linear Transformations 711

APPENDIX C PROOF OF THE NARROWBAND NOISEMODEL 712

APPENDIX D ZERO-CROSSING AND ORIGIN ENCIRCLEMENTSTATISTICS 714

D.1 The Zero-Crossing Problem 714

D.2 Average Rate of Zero Crossings 716

Problems 719

APPENDIX E CHI-SQUARE STATISTICS 720

APPENDIX F MATHEMATICAL AND NUMERICAL TABLES 722

F.1 The Gaussian Q-Function 722

F.2 Trigonometric Identities 724

F.3 Series Expansions 724

F.4 Integrals 725

F.4.1 Indefinite 725

F.4.2 Definite 726

F.5 Fourier-Transform Pairs 727

F.6 Fourier-Transform Theorems 727

APPENDIX G ANSWERS TO DRILL PROBLEMS

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BIBLIOGRAPHY

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INDEX 728

  • The updated seventh edition of Principles of Communications presents readers with a more supportive framework for learning through additional in-chapter examples.
  • Chapter 3, basic modulation techniques, has been split into linear modulation techniques, angle modulation and multiplexing.
  • Readers are exposed to digital data transmission techniques earlier in the book, so they can appreciate the characteristics of digital communication systems prior to learning about probability and stochastic processes.
  • They will also find expanded forward error correction code examples, and additional MATLAB problems.
  • Updated references and chapter on Information Theory and Coding.
  • Additional material on wireless topics, such as WiFi and MIMO channels.
  • Examples illustrating key points are included in each chapter, providing students with illustrations of how to apply theory developed in the text and Assisting instructors in development of material to be used in future chapters. 
  • Computational computer examples are included in each chapter, illustrating the use of the computer for calculation of various performance curves, and allowing students to reproduce, or add to, performance curves included in text. This encourages students to illustrate concepts rather than simply to accept them as shown. 
  • Chapter summaries are arranged as bulleted lists, providing a concise checklist for students’ exam review and for instructors’ lecture preparation. 
  • A wide variety of are problems included at the end of each chapter, categorized according to section, Allowing student to practice on concepts given in text. This also allows instructors to make assignments to check on the students' understanding of concepts presented in class.