Digital Communications

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 Blackman–Harris 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.

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
4.9 Symbol Error Performance for M-ary Systems (M > 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 REED–SOLOMON 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 Reed–Solomon Codes     421
    7.6.1 Reed–Solomon Error Probability     423
    7.6.2 Why R–S Codes Perform Well Against Burst Noise     426
    7.6.3 R–S Performance as a Function of Size, Redundancy, and Code Rate     426
    7.6.4 Finite Fields     429
    7.6.5 Reed–Solomon Encoding     435
    7.6.6 Reed–Solomon 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 Shannon–Hartley 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-Tap Prediction     853
    13.3.3 N-Tap Prediction     854
    13.3.4 Delta Modulation     856
    13.3.5 S-D Modulation     858
    13.3.6 S-D A-to-D Converter (ADC)     862
    13.3.7 S-D D-to-A Converter (DAC)     863
13.4 Adaptive Prediction     865
    13.4.1 Forward Adaptation     865
    13.4.2 Synthesis/Analysis Coding     866
13.5 Block Coding     868
    13.5.1 Vector Quantizing     868
13.6 Transform Coding     870
    13.6.1 Quantization for Transform Coding     872
    13.6.2 Subband Coding     872
13.7 Source Coding for Digital Data     873
    13.7.1 Properties of Codes     875
    13.7.2 Huffman Code     877
    13.7.3 Run-Length Codes     880
13.8 Examples of Source Coding     884
    13.8.1 Audio Compression     884
    13.8.2 Image Compression     889
13.9 Conclusion     898
Chapter 14  FADING CHANNELS     905
14.1 The Challenge of Communicating over Fading Channels     906
14.2 Characterizing Mobile-Radio Propagation     907
    14.2.1 Large-Scale Fading     912
    14.2.2 Small-Scale Fading     914
14.3 Signal Time Spreading     918
    14.3.1 Signal Time Spreading Viewed in the Time-Delay Domain     918
    14.3.2 Signal Time Spreading Viewed in the Frequency Domain     920
    14.3.3 Examples of Flat Fading and Frequency-Selective Fading     924
14.4 Time Variance of the Channel Caused by Motion     926
    14.4.1 Time Variance Viewed in the Time Domain     926
    14.4.2 Time Variance Viewed in the Doppler-Shift Domain     929
    14.4.3 Performance over a Slow- and Flat-Fading Rayleigh Channel     935
14.5 Mitigating the Degradation Effects of Fading     937
    14.5.1 Mitigation to Combat Frequency-Selective Distortion     939
    14.5.2 Mitigation to Combat Fast-Fading Distortion     942
    14.5.3 Mitigation to Combat Loss in SNR     942
    14.5.4 Diversity Techniques     944
    14.5.5 Modulation Types for Fading Channels     946
    14.5.6 The Role of an Interleaver     947
14.6 Summary of the Key Parameters Characterizing Fading Channels     951
    14.6.1 Fast-Fading Distortion: Case 1     951
    14.6.2 Frequency-Selective Fading Distortion: Case 2     952
    14.6.3 Fast-Fading and Frequency-Selective Fading
    Distortion: Case 3     953
14.7 Applications: Mitigating the Effects of Frequency-Selective Fading     955
    14.7.1 The Viterbi Equalizer as Applied to GSM     955
    14.7.2 The Rake Receiver Applied to Direct-Sequence Spread-Spectrum (DS/SS) Systems     958
14.8 Conclusion     960
Chapter 15  THE ABCs OF OFDM (ORTHOGONAL
FREQUENCY- DIVISION MULTIPLEXING)     971
15.1 What Is OFDM?     972
15.2 Why OFDM?     972
15.3 Getting Started with OFDM     973
15.4 Our Wish List (Preference for Flat Fading and Slow Fading)     974
    15.4.1 OFDM's Most Important Contribution to Communications over Multipath Channels     975
15.5 Conventional Multi-Channel FDM versus Multi-Channel OFDM     976
15.6 The History of the Cyclic Prefix (CP)     977
    15.6.1 Examining the Lengthened Symbol in OFDM     978
    15.6.2 The Length of the CP     979
15.7 OFDM System Block Diagram     979
15.8 Zooming in on the IDFT     981
15.9 An Example of OFDM Waveform Synthesis     981
15.10 Summarizing OFDM Waveform Synthesis     983
15.11 Data Constellation Points Distributed over the Subcarrier Indexes     984
    15.11.1 Signal Processing in the OFDM Receiver     986
    15.11.2 OFDM Symbol-Time Duration     986
    15.11.3 Why DC Is Not Used as a Subcarrier in Real Systems     987
15.12 Hermitian Symmetry     987
15.13 How Many Subcarriers Are Needed?     989
15.14 The Importance of the Cyclic Prefix (CP) in OFDM     989
    15.14.1 Properties of Continuous and Discrete Fourier Transforms     990
    15.14.2 Reconstructing the OFDM Subcarriers     991
    15.14.3 A Property of the Discrete Fourier Transform (DFT)     992
    15.14.4 Using Circular Convolution for Reconstructing an OFDM Subcarrier     993
    15.14.5 The Trick That Makes Linear Convolution Appear
    Circular     994
15.15 An Early OFDM Application: Wi-Fi Standard 802.11a     997
    15.15.1 Why the Transform Size N Needs to Be Larger Than the Number of Subcarriers     999
15.16 Cyclic Prefix (CP) and Tone Spacing     1000
15.17 Long-Term Evolution (LTE) Use of OFDM     1001
    15.17.1 LTE Resources: Grid, Block, and Element     1002
    15.17.2 OFDM Frame in LTE     1003
15.18 Drawbacks of OFDM     1006
    15.18.1 Sensitivity to Doppler     1006
    15.18.2 Peak-to-Average Power Ratio (PAPR) and SC-OFDM     1006
    15.18.3 Motivation for Reducing PAPR     1007
15.19 Single-Carrier OFDM (SC-OFDM) for Improved PAPR Over Standard OFDM     1007
    15.19.1 SC-OFDM Signals Have Short Mainlobe Durations     1010
    15.19.2 Is There an Easier Way to Implement SC-OFDM?     1011
15.20 Conclusion     1012
Chapter 16  THE MAGIC OF MIMO (MULTIPLE INPUT/MULTIPLE OUTPUT)     1017
16.1 What is MIMO?     1018
    16.1.1 MIMO Historical Perspective     1019
    16.1.2 Vectors and Phasors     1019
    16.1.3 MIMO Channel Model     1020
16.2 Various Benefits of Multiple Antennas     1023
    16.2.1 Array Gain     1023
    16.2.2 Diversity Gain     1023
    16.2.3 SIMO Receive Diversity Example     1026
    16.2.4 MISO Transmit Diversity Example     1027
    16.2.5 Two-Time Interval MISO Diversity Example     1028
    16.2.6 Coding Gain     1029
    16.2.7 Visualization of Array Gain, Diversity Gain, and Coding Gain     1029
16.3 Spatial Multiplexing     1031
    16.3.1 Basic Idea of MIMO-Spatial Multiplexing (MIMO-SM)     1031
    16.3.2 Analogy Between MIMO-SM and CDMA     1033
    16.3.3 When Only the Receiver Has Channel-State Information (CSI)     1033
    16.3.4 Impact of the Channel Model     1034
    16.3.5 MIMO and OFDM Form a Natural Coupling     1036
16.4 Capacity Performance     1037
    16.4.1 Deterministic Channel Modeling     1038
    16.4.2 Random Channel Models     1040
16.5 Transmitter Channel-State Information (CSI)     1042
    16.5.1 Optimum Power Distribution     1044
16.6 Space-Time Coding     1047
    16.6.1 Block Codes in MIMO Systems     1047
    16.6.2 Trellis Codes in MIMO Systems     1050
16.7 MIMO Trade-Offs     1051
    16.7.1 Fundamental Trade-Off     1051
    16.7.2 Trade-Off Yielding Greater Robustness for PAM and QAM     1052
    16.7.3 Trade-Off Yielding Greater Capacity for PAM and QAM     1053
    16.7.4 Tools for Trading Off Multiplexing Gain and Diversity Gain     1054
16.8 Multi-User MIMO (MU-MIMO)     1058
    16.8.1 What Is MU-MIMO?     1059
    16.8.2 SU-MIMO and MU-MIMO Notation     1059
    16.8.3 A Real Shift in MIMO Thinking     1061
    16.8.4 MU-MIMO Capacity     1067
    16.8.5 Sum-Rate Capacity Comparison for Various Precoding Strategies     1081
    16.8.6 MU-MIMO Versus SU-MIMO Performance     1082
16.9 Conclusion     1083
Index     1089

ONLINE ONLY:
Chapter 17  Encryption and Decryption
Appendix A  A Review of Fourier Techniques
Appendix B  Fundamentals of Statistical Decision Theory
Appendix C  Response of a Correlator to White Noise
Appendix D  Often-Used Identities
Appendix E  S-Domain, Z-Domain, and Digital Filtering
Appendix F  OFDM Symbol Formation with an N-Point Inverse Discrete Fourier Transform (IDFT)
Appendix G  List of Symbols