Fredric J. Harris, Bernard Sklar

Digital Communications

Fundamentals and Applications

• Gebundenes Buch

Produktbeschreibung

With remarkable clarity, Drs. Bernard Sklar and fred harris introduce every digital communication technology at the heart of todays wireless and Internet revolutions, with completely new chapters on synchronisation, OFDM, and MIMO. Building on the fields classic, best-selling introduction, the authors provide a unified structure and context for helping students and professional engineers understand each technology, without sacrificing mathematical precision. They illuminate the big picture and details of modulation, coding, and signal processing, tracing signals and processing steps from information source through sink. Throughout, readers will find numeric examples, step-by-step implementation guidance, and diagrams that place key concepts in clear context. * Understand signals, spectra, modulation, demodulation, detection, communication links, system link budgets, synchronisation, fading, and other key concepts * Apply channel coding techniques, including advanced turbo coding and LDPC * Explore multiplexing, multiple access, and spread spectrum concepts and techniques * Learn about source coding: amplitude quantising, differential PCM, and adaptive prediction * Discover the essentials and applications of synchronisation, OFDM, and MIMO technology

Produktdetails

• Verlag: Pearson / Prentice Hall
• 3. Aufl.
• Seitenzahl: 1136
• Erscheinungstermin: 24. Dezember 2020
• Englisch
• Abmessung: 237mm x 190mm x 64mm
• Gewicht: 1976g
• ISBN-13: 9780134588568
• ISBN-10: 0134588568
• Artikelnr.: 48434205

Autorenporträt

Dr. Bernard Sklar has over 40 years of experience in technical design and management positions at Republic Aviation, Hughes Aircraft, Litton Industries, and The Aerospace Corporation, where he helped develop the MILSTAR satellite system. He is now head of advanced systems at Communications Engineering Services, a consulting company he founded in 1984. He has taught engineering courses at several universities, including UCLA and USC, and has trained professional engineers worldwide. Dr. Fredric J. Harris is a professor of electrical engineering and the CUBIC signal processing chair at San Diego State University and an internationally renowned expert on DSP and communication systems. He is also the co-inventor of the BlackmanHarris filter. He has extensively published many technical papers, the most famous being the seminal 1978 paper On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform. He is also the author of the textbook Multi-Rate Signal Processing for Communication Systems and the source coding chapter in the previous edition of this book.

Inhaltsangabe

Preface xxiii
Chapter 1 SIGNALS AND SPECTRA 1
1.1 Digital Communication Signal Processing 2
1.1.1 Why Digital? 2
1.1.2 Typical Block Diagram and Transformations 4
1.1.3 Basic Digital Communication Nomenclature 7
1.1.4 Digital Versus Analog Performance Criteria 9
1.2 Classification of Signals 10
1.2.1 Deterministic and Random Signals 10
1.2.2 Periodic and Nonperiodic Signals 10
1.2.3 Analog and Discrete Signals 10
1.2.4 Energy and Power Signals 11
1.2.5 The Unit Impulse Function 12
1.3 Spectral Density 13
1.3.1 Energy Spectral Density 13
1.3.2 Power Spectral Density 14
1.4 Autocorrelation 15
1.4.1 Autocorrelation of an Energy Signal 10
1.4.2 Autocorrelation of a Periodic (Power) Signal 16
1.5 Random Signals 17
1.5.1 Random Variables 17
1.5.2 Random Processes 19
1.5.3 Time Averaging and Ergodicity 21
1.5.4 Power Spectral Density and Autocorrelation of a Random Process 22
1.5.5 Noise in Communication Systems 27
1.6 Signal Transmission Through Linear Systems 30
1.6.1 Impulse Response 30
1.6.2 Frequency Transfer Function 31
1.6.3 Distortionless Transmission 32
1.6.4 Signals, Circuits, and Spectra 39
1.7 Bandwidth of Digital Data 41
1.7.1 Baseband Versus Bandpass 41`
1.7.2 The Bandwidth Dilemma 44
1.8 Conclusion 47
Chapter 2 FORMATTING AND BASEBAND MODULATION 53
2.1 Baseband Systems 54
2.2 Formatting Textual Data (Character Coding) 55
2.3 Messages, Characters, and Symbols 55
2.3.1 Example of Messages, Characters, and Symbols 56
2.4 Formatting Analog Information 57
2.4.1 The Sampling Theorem 57
2.4.2 Aliasing 64
2.4.3 Why Oversample? 67
2.4.4 Signal Interface for a Digital System 69
2.5 Sources of Corruption 70
2.5.1 Sampling and Quantizing Effects 71
2.5.2 Channel Effects 71
2.5.3 Signal-to-Noise Ratio for Quantized Pulses 72
2.6 Pulse Code Modulation 73
2.7 Uniform and Nonuniform Quantization 75
2.7.1 Statistics of Speech Amplitudes 75
2.7.2 Nonuniform Quantization 77
2.7.3 Companding Characteristics 77
2.8 Baseband Transmission 79
2.8.1 Waveform Representation of Binary Digits 79
2.8.2 PCM Waveform Types 80
2.8.3 Spectral Attributes of PCM Waveforms 83
2.8.4 Bits per PCM Word and Bits per Symbol 84
2.8.5 M-ary Pulse-Modulation Waveforms 86
2.9 Correlative Coding 88
2.9.1 Duobinary Signaling 88
2.9.2 Duobinary Decoding 89
2.9.3 Precoding 90
2.9.4 Duobinary Equivalent Transfer Function 91
2.9.5 Comparison of Binary and Duobinary Signaling 93
2.9.6 Polybinary Signaling 94
2.10 Conclusion 94
Chapter 3 BASEBAND DEMODULATION/DETECTION 99
3.1 Signals and Noise 100
3.1.1 Error-Performance Degradation in Communication Systems 100
3.1.2 Demodulation and Detection 101
3.1.3 A Vectorial View of Signals and Noise 105
3.1.4 The Basic SNR Parameter for Digital Communication Systems 112
3.1.5 Why Eb /N0 Is a Natural Figure of Merit 113
3.2 Detection of Binary Signals in Gaussian Noise 114
3.2.1 Maximum Likelihood Receiver Structure 114
3.2.2 The Matched Filter 117
3.2.3 Correlation Realization of the Matched Filter 119
3.2.4 Optimizing Error Performance 122
3.2.5 Error Probability Performance of Binary Signaling 126
3.3 Intersymbol Interference 130
3.3.1 Pulse Shaping to Reduce ISI 133
3.3.2 Two Types of Error-Performance Degradation 136
3.3.3 Demodulation/Detection of Shaped Pulses 140
3.4 Equalization 144
3.4.1 Channel Characterization 144
3.4.2 Eye Pattern 145
3.4.3 Equalizer Filter Types 146
3.4.4 Preset and Adaptive Equalization 152
3.4.5 Filter Update Rate 155
3.5 Conclusion 156
Chapter 4 BANDPASS MODULATION AND DEMODULATION/DETECTION 161
4.1 Why Modulate? 162
4.2 Digital Bandpass Modulation Techniques 162
4.2.1 Phasor Representation of a Sinusoid 163
4.2.2 Phase-Shift Keying 166
4.2.3 Frequency-Shift Keying 167
4.2.4 Amplitude Shift Keying 167
4.2.5 Amplitude-Phase Keying 168
4.2.6 Waveform Amplitude Coefficient 168
4.3 Detection of Signals in Gaussian Noise 169
4.3.1 Decision Regions 169
4.3.2 Correlation Receiver 170
4.4 Coherent Detection 175
4.4.1 Coherent Detection of PSK 175
4.4.2 Sampled Matched Filter 176
4.4.3 Coherent Detection of Multiple Phase-Shift Keying 181
4.4.4 Coherent Detection of FSK 184
4.5 Noncoherent Detection 187
4.5.1 Detection of Differential PSK 187
4.5.2 Binary Differential PSK Example 188
4.5.3 Noncoherent Detection of FSK 190
4.5.4 Required Tone Spacing for Noncoherent Orthogonal FSK Signaling 192
4.6 Complex Envelope 196
4.6.1 Quadrature Implementation of a Modulator 197
4.6.2 D8PSK Modulator Example 198
4.6.3 D8PSK Demodulator Example 200
4.7 Error Performance for Binary Systems 202
4.7.1 Probability of Bit Error for Coherently Detected BPSK 202
4.7.2 Probability of Bit Error for Coherently Detected, Differentially
Encoded Binary PSK 204
4.7.3 Probability of Bit Error for Coherently Detected Binary Orthogonal
FSK 204
4.7.4 Probability of Bit Error for Noncoherently Detected Binary Orthogonal
FSK 206
4.7.5 Probability of Bit Error for Binary DPSK 208
4.7.6 Comparison of Bit-Error Performance for Various Modulation Types 210
4.8 M-ary Signaling and Performance 211
4.8.1 Ideal Probability of Bit-Error Performance 211
4.8.2 M-ary Signaling 212
4.8.3 Vectorial View of MPSK Signaling 214
4.8.4 BPSK and QPSK Have the Same Bit-Error Probability 216
4.8.5 Vectorial View of MFSK Signaling 217
> 2) 221
4.9.1 Probability of Symbol Error for MPSK 221
4.9.2 Probability of Symbol Error for MFSK 222
4.9.3 Bit-Error Probability Versus Symbol Error Probability for Orthogonal
Signals 223
4.9.4 Bit-Error Probability Versus Symbol Error Probability for
Multiple-Phase Signaling 226
4.9.5 Effects of Intersymbol Interference 228
4.10 Conclusion 228
Chapter 5 COMMUNICATIONS LINK ANALYSIS 235
5.1 What the System Link Budget Tells the System Engineer 236
5.2 The Channel 236
5.2.1 The Concept of Free Space 237
5.2.2 Error-Performance Degradation 237
5.2.3 Sources of Signal Loss and Noise 238
5.3 Received Signal Power and Noise Power 243
5.3.1 The Range Equation 243
5.3.2 Received Signal Power as a Function of Frequency 247
5.3.3 Path Loss Is Frequency Dependent 248
5.3.4 Thermal Noise Power 250
5.4 Link Budget Analysis 252
5.4.1 Two Eb /N0 Values of Interest 254
5.4.2 Link Budgets Are Typically Calculated in Decibels 256
5.4.3 How Much Link Margin Is Enough? 257
5.4.4 Link Availability 258
5.5 Noise Figure, Noise Temperature, and System Temperature 263
5.5.1 Noise Figure 263
5.5.2 Noise Temperature 265
5.5.3 Line Loss 266
5.5.4 Composite Noise Figure and Composite Noise Temperature 269
5.5.5 System Effective Temperature 270
5.5.6 Sky Noise Temperature 275
5.6 Sample Link Analysis 279
5.6.1 Link Budget Details 279
5.6.2 Receiver Figure of Merit 282
5.6.3 Received Isotropic Power 282
5.7 Satellite Repeaters 283
5.7.1 Nonregenerative Repeaters 283
5.7.2 Nonlinear Repeater Amplifiers 288
5.8 System Trade-Offs 289
5.9 Conclusion 290
Chapter 6 CHANNEL CODING: PART 1: WAVEFORM CODES AND BLOCK CODES 297
6.1 Waveform Coding and Structured Sequences 298
6.1.1 Antipodal and Orthogonal Signals 298
6.1.2 M-ary Signaling 300
6.1.3 Waveform Coding 300
6.1.4 Waveform-Coding System Example 304
6.2 Types of Error Control 307
6.2.1 Terminal Connectivity 307
6.2.2 Automatic Repeat Request 307
6.3 Structured Sequences 309
6.3.1 Channel Models 309
6.3.2 Code Rate and Redundancy 311
6.3.3 Parity-Check Codes 312
6.3.4 Why Use Error-Correction Coding? 315
6.4 Linear Block Codes 320
6.4.1 Vector Spaces 320
6.4.2 Vector Subspaces 321
6.4.3 A (6, 3) Linear Block Code Example 322
6.4.4 Generator Matrix 323
6.4.5 Systematic Linear Block Codes 325
6.4.6 Parity-Check Matrix 326
6.4.7 Syndrome Testing 327
6.4.8 Error Correction 329
6.4.9 Decoder Implementation 332
6.5 Error-Detecting and Error-Correcting Capability 334
6.5.1 Weight and Distance of Binary Vectors 334
6.5.2 Minimum Distance of a Linear Code 335
6.5.3 Error Detection and Correction 335
6.5.4 Visualization of a 6-Tuple Space 339
6.5.5 Erasure Correction 341
6.6 Usefulness of the Standard Array 342
6.6.1 Estimating Code Capability 342
6.6.2 An (n, k) Example 343
6.6.3 Designing the (8, 2) Code 344
6.6.4 Error Detection Versus Error Correction Trade-Offs 345
6.6.5 The Standard Array Provides Insight 347
6.7 Cyclic Codes 349
6.7.1 Algebraic Structure of Cyclic Codes 349
6.7.2 Binary Cyclic Code Properties 351
6.7.3 Encoding in Systematic Form 352
6.7.4 Circuit for Dividing Polynomials 353
6.7.5 Systematic Encoding with an (n ? k)-Stage Shift Register 356
6.7.6 Error Detection with an (n ? k)-Stage Shift Register 358
6.8 Well-Known Block Codes 359
6.8.1 Hamming Codes 359
6.8.2 Extended Golay Code 361
6.8.3 BCH Codes 363
6.9 Conclusion 367
Chapter 7 CHANNEL CODING: PART 2: CONVOLUTIONAL CODES AND REEDSOLOMON CODES
375
7.1 Convolutional Encoding 376
7.2 Convolutional Encoder Representation 378
7.2.1 Connection Representation 378
7.2.2 State Representation and the State Diagram 382
7.2.3 The Tree Diagram 385
7.2.4 The Trellis Diagram 385
7.3 Formulation of the Convolutional Decoding Problem 388
7.3.1 Maximum Likelihood Decoding 388
7.3.2 Channel Models: Hard Versus Soft Decisions 390
7.3.3 The Viterbi Convolutional Decoding Algorithm 394
7.3.4 An Example of Viterbi Convolutional Decoding 394
7.3.5 Decoder Implementation 398
7.3.6 Path Memory and Synchronization 401
7.4 Properties of Convolutional Codes 402
7.4.1 Distance Properties of Convolutional Codes 402
7.4.2 Systematic and Nonsystematic Convolutional Codes 406
7.4.3 Catastrophic Error Propagation in Convolutional Codes 407
7.4.4 Performance Bounds for Convolutional Codes 408
7.4.5 Coding Gain 409
7.4.6 Best-Known Convolutional Codes 411
7.4.7 Convolutional Code Rate Trade-Off 413
7.4.8 Soft-Decision Viterbi Decoding 413
7.5 Other Convolutional Decoding Algorithms 415
7.5.1 Sequential Decoding 415
7.5.2 Comparisons and Limitations of Viterbi and Sequential Decoding 418
7.5.3 Feedback Decoding 419
7.6 ReedSolomon Codes 421
7.6.1 ReedSolomon Error Probability 423
7.6.2 Why RS Codes Perform Well Against Burst Noise 426
7.6.3 RS Performance as a Function of Size, Redundancy, and Code Rate 426
7.6.4 Finite Fields 429
7.6.5 ReedSolomon Encoding 435
7.6.6 ReedSolomon Decoding 439
7.7 Interleaving and Concatenated Codes 446
7.7.1 Block Interleaving 449
7.7.2 Convolutional Interleaving 452
7.7.3 Concatenated Codes 453
7.8 Coding and Interleaving Applied to the Compact Disc Digital Audio
System 454
7.8.1 CIRC Encoding 456
7.8.2 CIRC Decoding 458
7.8.3 Interpolation and Muting 460
7.9 Conclusion 462
Chapter 8 CHANNEL CODING: PART 3: TURBO CODES AND LOW-DENSITY PARITY CHECK
(LDPC) CODES 471
8.1 Turbo Codes 472
8.1.1 Turbo Code Concepts 472
8.1.2 Log-Likelihood Algebra 476
8.1.3 Product Code Example 477
8.1.4 Encoding with Recursive Systematic Codes 484
8.1.5 A Feedback Decoder 489
8.1.6 The MAP Algorithm 493
8.1.7 MAP Decoding Example 499
8.2 Low-Density Parity Check (LDPC) Codes 504
8.2.1 Background and Overview 504
8.2.2 The Parity-Check Matrix 505
8.2.3 Finding the Best-Performing Codes 507
8.2.4 Decoding: An Overview 509
8.2.5 Mathematical Foundations 514
8.2.6 Decoding in the Probability Domain 518
8.2.7 Decoding in the Logarithmic Domain 526
8.2.8 Reduced-Complexity Decoders 531
8.2.9 LDPC Performance 532
8.2.10 Conclusion 535
Appendix 8A: The Sum of Log-Likelihood Ratios 535
Appendix 8B: Using Bayes Theorem to Simplify the Bit Conditional
Probability 537
Appendix 8C: Probability that a Binary Sequence Contains an Even Number of
Ones 537
Appendix 8D: Simplified Expression for the Hyperbolic Tangent of the
Natural Log of a Ratio of Binary Probabilities 538
Appendix 8E: Proof that phi(x) = phi^-1(x) 538
Appendix 8F: Bit Probability Initialization 539
Chapter 9 MODULATION AND CODING TRADE-OFFS 549
9.1 Goals of the Communication System Designer 550
9.2 Error-Probability Plane 550
9.3 Nyquist Minimum Bandwidth 552
9.4 ShannonHartley Capacity Theorem 554
9.4.1 Shannon Limit 556
9.4.2 Entropy 557
9.4.3 Equivocation and Effective Transmission Rate 560
9.5 Bandwidth-Efficiency Plane 562
9.5.1 Bandwidth Efficiency of MPSK and MFSK Modulation 563
9.5.2 Analogies Between the Bandwidth-Efficiency and Error-Probability
Planes 564
9.6 Modulation and Coding Trade-Offs 565
9.7 Defining, Designing, and Evaluating Digital Communication
Systems 566
9.7.1 M-ary Signaling 567
9.7.2 Bandwidth-Limited Systems 568
9.7.3 Power-Limited Systems 569
9.7.4 Requirements for MPSK and MFSK Signaling 570
9.7.5 Bandwidth-Limited Uncoded System Example 571
9.7.6 Power-Limited Uncoded System Example 573
9.7.7 Bandwidth-Limited and Power-Limited Coded System Example 575
9.8 Bandwidth-Efficient Modulation 583
9.8.1 QPSK and Offset QPSK Signaling 583
9.8.2 Minimum-Shift Keying 587
9.8.3 Quadrature Amplitude Modulation 591
9.9 Trellis-Coded Modulation 594
9.9.1 The Idea Behind Trellis-Coded Modulation 595
9.9.2 TCM Encoding 597
9.9.3 TCM Decoding 601
9.9.4 Other Trellis Codes 604
9.9.5 Trellis-Coded Modulation Example 606
9.9.6 Multidimensional Trellis-Coded Modulation 610
9.10 Conclusion 610
Chapter 10 SYNCHRONIZATION 619
10.1 Receiver Synchronization 620
10.1.1 Why We Must Synchronize 620
10.1.2 Alignment at the Waveform Level and Bit Stream Level 620
10.1.3 Carrier-Wave Modulation 620
10.1.4 Carrier Synchronization 621
10.1.5 Symbol Synchronization 624
10.1.6 Eye Diagrams and Constellations 625
10.2 Synchronous Demodulation 626
10.2.1 Minimizing Energy in the Difference Signal 628
10.2.2 Finding the Peak of the Correlation Function 629
10.2.3 The Basic Analog Phase-Locked Loop (PLL) 631
10.2.4 Phase-Locking Remote Oscillators 631
10.2.5 Estimating Phase Slope (Frequency) 633
10.3 Loop Filters, Control Circuits, and Acquisition 634
10.3.1 How Many Loop Filters Are There in a System? 634
10.3.2 The Key Loop Filters 634
10.3.3 Why We Want R Times R-dot 634
10.3.4 The Phase Error S-Curve 636
10.4 Phase-Locked Loop Timing Recovery 637
10.4.1 Recovering Carrier Timing from a Modulated Waveform 637
10.4.2 Classical Timing Recovery Architectures 638
10.4.3 Timing-Error Detection: Insight from the Correlation Function 641
10.4.4 Maximum-Likelihood Timing-Error Detection 642
10.4.5 Polyphase Matched Filter and Derivative Matched Filter 643
10.4.6 Approximate ML Timing Recovery PLL for a 32-Path PLL 647
10.5 Frequency Recovery Using a Frequency-Locked Loop (FLL) 652
10.5.1 Band-Edge Filters 654
10.5.2 Band-Edge Filter Non-Data-Aided Timing Synchronization 660
10.6 Effects of Phase and Frequency Offsets 664
10.6.1 Phase Offset and No Spinning: Effect on Constellation 665
10.6.2 Slow Spinning Effect on Constellation 667
10.6.3 Fast Spinning Effect on Constellation 670
10.7 Conclusion 672
Chapter 11 MULTIPLEXING AND MULTIPLE ACCESS 681
11.1 Allocation of the Communications Resource 682
11.1.1 Frequency-Division Multiplexing/Multiple Access 683
11.1.2 Time-Division Multiplexing/Multiple Access 688
11.1.3 Communications Resource Channelization 691
11.1.4 Performance Comparison of FDMA and TDMA 692
11.1.5 Code-Division Multiple Access 695
11.1.6 Space-Division and Polarization-Division Multiple Access 698
11.2 Multiple-Access Communications System and Architecture 700
11.2.1 Multiple-Access Information Flow 701
11.2.2 Demand-Assignment Multiple Access 702
11.3 Access Algorithms 702
11.3.1 ALOHA 702
11.3.2 Slotted ALOHA 705
11.3.3 Reservation ALOHA 706
11.3.4 Performance Comparison of S-ALOHA and R-ALOHA 708
11.3.5 Polling Techniques 710
11.4 Multiple-Access Techniques Employed with INTELSAT 712
11.4.1 Preassigned FDM/FM/FDMA or MCPC Operation 713
11.4.2 MCPC Modes of Accessing an INTELSAT Satellite 713
11.4.3 SPADE Operation 716
11.4.4 TDMA in INTELSAT 721
11.4.5 Satellite-Switched TDMA in INTELSAT 727
11.5 Multiple-Access Techniques for Local Area Networks 731
11.5.1 Carrier-Sense Multiple-Access Networks 731
11.5.2 Token-Ring Networks 733
11.5.3 Performance Comparison of CSMA/CD and Token-Ring Networks 734
11.6 Conclusion 736
Chapter 12 SPREAD-SPECTRUM TECHNIQUES 741
12.1 Spread-Spectrum Overview 742
12.1.1 The Beneficial Attributes of Spread-Spectrum Systems 742
12.1.2 A Catalog of Spreading Techniques 746
12.1.3 Model for Direct-Sequence Spread-Spectrum Interference Rejection 747
12.1.4 Historical Background 748
12.2 Pseudonoise Sequences 750
12.2.1 Randomness Properties 750
12.2.2 Shift Register Sequences 750
12.2.3 PN Autocorrelation Function 752
12.3 Direct-Sequence Spread-Spectrum Systems 753
12.3.1 Example of Direct Sequencing 755
12.3.2 Processing Gain and Performance 756
12.4 Frequency-Hopping Systems 759
12.4.1 Frequency-Hopping Example 761
12.4.2 Robustness 762
12.4.3 Frequency Hopping with Diversity 762
12.4.4 Fast Hopping Versus Slow Hopping 763
12.4.5 FFH/MFSK Demodulator 765
12.4.6 Processing Gain 766
12.5 Synchronization 766
12.5.1 Acquisition 767
12.5.2 Tracking 772
12.6 Jamming Considerations 775
12.6.1 The Jamming Game 775
12.6.2 Broadband Noise Jamming 780
12.6.3 Partial-Band Noise Jamming 781
12.6.4 Multiple-Tone Jamming 783
12.6.5 Pulse Jamming 785
12.6.6 Repeat-Back Jamming 787
12.6.7 BLADES System 788
12.7 Commercial Applications 789
12.7.1 Code-Division Multiple Access 789
12.7.2 Multipath Channels 792
12.7.3 The FCC Part 15 Rules for Spread-Spectrum Systems 793
12.7.4 Direct Sequence Versus Frequency Hopping 794
12.8 Cellular Systems 796
12.8.1 Direct-Sequence CDMA 796
12.8.2 Analog FM Versus TDMA Versus CDMA 799
12.8.3 Interference-Limited Versus Dimension-Limited Systems 801
12.8.4 IS-95 CDMA Digital Cellular System 803
12.9 Conclusion 814
Chapter 13 SOURCE CODING 823
13.1 Sources 824
13.1.1 Discrete Sources 824
13.1.2 Waveform Sources 829
13.2 Amplitude Quantizing 830
13.2.1 Quantizing Noise 833
13.2.2 Uniform Quantizing 836
13.2.3 Saturation 840
13.2.4 Dithering 842
13.2.5 Nonuniform Quantizing 845
13.3 Pulse Code Modulation 849
13.3.1 Differential Pulse Code Modulation 850
13.3.2 One-Ta