`
`Exhibit 1018
`
`US. Patent NO. 6,108,388
`
`Exhibit 1018
`U.S. Patent No. 6,108,388
`
`

`

` i l1
`
`Library of Congress Cataloging-in-Publication Data
`
`SKLAR, BERNARD (date)
`Digital communications.
`
`Bibliography: 13.
`Includes index.
`1. Title.
`1. Digital communications.
`TK5103.7.SSS
`1988
`621.38’0413
`ISBN 0-13-211939-0
`
`87-1316
`
`..-.-mi
`
`Editorial/production supervision and
`interior design: Reynold Rieger
`Cover design: Wanda Lubelska Design
`Manufacturing buyers: Gordon Osbourne and Paula Beneyento
`
`._
`
`© 1988 by Prentice Hall
`A Division of Simon & Schuster
`
`Englewood Cliffs, New Jersey 07632
`
`All rights reserved. No part of this book may be
`reproduced, in any form or by any means,
`without permission in writing from the publisher.
`
`Printed in the United States of America
`
`10987654321
`
`ISBN U-lB-Ell‘lB‘l-U DES
`
`Prentice-Hall International (UK) Limited, London
`Prentice-Hall of Australia Pty. Limited, Sydney
`Prentice—Hall Canada Inc., Toronto
`Prentice—Hall Hispanoamericana, S.A., Mexico
`Prentice-Hall of India Private Limited, New Delhi
`Prentice-Hall of Japan, Inc., Tokyo
`Simon & Schuster Asia Pte. Ltd., Singapore
`Editora Prentice-Hall do Brasil, Ltda., Rio de Janeiro
`
`

`

`
`
`DIGITAL
`
`COMMUNICATIONS
`
`Englewood Cliffs, New Jersey 07632
`
`Fufiémefialfi and Apglicatiens
`
`BERNARD SKLéR
`
`The Aerospace Corporation, El Segundo, California
`and
`
`University of California, Los Angeles
`
`PRENTICE HALL
`
`

`

`1
`
`Contents
`
`PREFACE
`
`xxi
`
`SIGNALS AND SPECTRA
`
`4 9
`
`11
`
`17
`
`Digital Communication Signal Processing,
`1.1.1 Why Digital?,
`3
`1.1.2
`Typical Block Diagram and Transformations,
`1.1.3 Basic Digital Communication Nomenclature,
`1.1.4 Digital versus Analog Performance Criteria,
`Classification of Signals,
`11
`1.2.1 Deterministic and Random Signals,
`1.2.2
`Periodic and Nonperiodic Signals,
`1.2.3 Analog and Discrete Signals,
`12
`1.2.4
`Energy and Power Signals,
`1.2.5
`The Unit Impulse Function,
`
`3
`
`11
`12
`
`12
`13
`
`1.1
`
`1.2
`
`1.3
`
`1.5
`
`14
`Spectral Density,
`1.3.1'
`Energy Spectral Density,
`1.3.2
`Power Spectral Density,
`1.4 Autocorrelation,
`17
`17
`1.4.1 Aatocorrelation of an Energy Signal,
`1.4.2 Aatocorrelation of a Periodic (Power) Signal,
`Random Signals,
`18
`1.5.]
`Random Variables,
`1.5.2
`Random Processes,
`
`14
`15
`
`18
`20
`
`
`
`

`

`22
`Time Averaging and Ergodicity,
`1.5.3
`Power Spectral Density ofa Random Process,
`1.5.4
`1.5.5 Noise in Communication Systems,
`27
`Signal Transmission through Linear Systems,
`1.6.1
`Impulse Response,
`31
`31
`1.6.2
`Frequency Transfer Function,
`32
`1.6.3 Distortionless Transmission,
`1.6.4
`Signals, Circuits, and Spectra,
`Bandwidth of Digital Data,
`41
`1.7.1
`Baseband versus Bandpass,
`1.7.2
`The Bandwidth Dilemma,
`
`38
`
`41
`
`43
`
`23
`
`30
`
`Conclusion,
`References,
`Problems,
`
`46
`46
`47
`
`1.6
`
`1.7
`
`1.8
`
`2 FORMATTING AND BASEBAND TRANSMISSION
`
`51
`
`54
`Baseband Systems,
`2.1
`Formatting Textual Data (Character Coding),
`2.2
`2.3 Messages, Characters, and Symbols,
`55
`2.3.1
`Example of Messages, Characters, and
`Symbols,
`55
`Formatting Analog Information,
`2.4.1
`The Sampling Theorem,
`2.4.2 Aliasing,
`66
`2.4.3
`Signal Interface for a Digital System,
`
`2.4
`
`59
`
`59
`
`55
`
`69
`
`2.5
`
`2.6
`
`70
`Sources of Corruption,
`2.5.]
`Sampling and Quantizing Effects,
`2.5.2
`Channel Effects,
`71
`2.5.3
`Signal-to-Noise Ratio for Quantized Pulses,
`Pulse Code Modulation,
`‘73
`
`70
`
`72
`
`2.8
`
`2.7 Uniform and Nonuniform Quantization,
`2.7.1
`Statistics of Speech Amplitudes,
`74
`2.7.2 Nonuniform Quantization,
`76
`2.7.3
`Companding Characteristics,
`Baseband Transmission,
`78
`2.8.1 Waveform Representation ofBinary Digits.
`2.8.2
`PCM Waveform Types,
`78
`82
`2.8.3
`Spectral Attributes ofPCM Waveforms,
`2.9 Detection of Binary Signals in Gaussian Noise,
`2.9.1 Maximum Likelihood Receiver Structure,
`2.9.2
`The Matched Filter,
`88
`2.9.3
`Correlation Realization of the Matched Filter.
`2.9.4 Application of the Matched Filter,
`91
`2.9.5
`Error Probability Performance of Binary
`Signaling,
`92
`
`74
`
`77
`
`78
`
`83
`
`85
`
`90
`
`viii
`
`Contents
`
`
`
`#
`
`

`

`“I
`
`2.10 Multilevel Baseband Transmission,
`2.10.1 PCM WordSize,
`97
`
`95
`
`2.11
`
`100
`
`98
`lntersymbol Interference,
`2.11.1 Pulse Shaping to Reduce IS],
`2.11.2 Equalization,
`104
`2.12 Partial Response Signaling,
`2.12.1 Duobinary Signaling,
`2.12.2 Duobinary Decoding,
`2.12.3 Precoding,
`108
`2.12.4 Duobinary Equivalent Transfer Function,
`2.12.5 Comparison of Binary with Duobinary
`Signaling,
`111
`2.12.6 Polybinary Signaling,
`2.13 Conclusion,
`112
`References,
`113
`Problems,
`113
`
`106
`106
`107
`
`112
`
`109
`
`51
`
`3 BANDPASS MODULATION AND DEMODULATION
`
`3.3
`
`118
`3.1 Why Modulate?,
`119
`3.2
`Signals and Noise,
`119
`3.2.1
`Noise in Radio Communication Systems,
`120
`3.2.2
`A Geometric View of Signals and Noise,
`Digital Bandpass Modulation Techniques,
`127
`3.3.1
`Phase Shift Keying,
`130
`130
`3.3.2
`Frequency Shift Keying,
`131
`3.3.3 Amplitude Shift Keying,
`131
`3.3.4 Amplitude Phase Keying,
`3.3.5 Waveform Amplitude Coefficient,
`3.4 Detection of Signals in Gaussian Noise,
`3.4.1 Decision Regions,
`132
`3.4.2
`Correlation Receiver,
`
`132
`132
`
`133
`
`3.5
`
`138
`Coherent Detection,
`3.5.1
`Coherent Detection ofPSK,
`3.5.2
`Sampled Matched Filter,
`139
`3.5.3
`Coherent Detection of Multiple Phase Shift
`Keying,
`142
`Coherent Detection ofFSK,
`3.5.4
`3.6 Noncoherent Detection,
`146
`146
`3.6.1 Detection ofDifferential PSK,
`148
`3.6.2
`Binary Differential PSK Example,
`150
`3.6.3 Noncoherent Detection ofFSK,
`3.6.4 Minimum Required Tone Spacing for Noncoherent
`Orthogonal FSK Signaling,
`152
`
`138
`
`145
`
`“5
`
`Contents
`
`117
`
`ix
`
`
`
`

`

`3.7
`
`3.7.2
`
`3.7.3
`
`3.7.4
`
`3.7.5
`3.7.6
`
`3.9
`
`3.9.4
`
`155
`Error Performance for Binary Systems,
`3.7.1
`Probability of Bit Error for Coherently Detected
`BPSK,
`155
`Probability of Bit Error for Coherently Detected
`Dfierentially Encoded PSK,
`160
`Probability of Bit Error for Coherently Detected
`FSK,
`161
`Probability of Bit Error for Noncoherently Detected
`FSK,
`162
`164
`Probability of Bit Error for DPSK,
`Comparison of Bit Error Performance for Various
`Modulation Types,
`166
`167
`3.8 M-ary Signaling and Performance,
`3.8.1
`Ideal Probability of Bit Error Performance,
`3.8.2 M-ary Signaling,
`167
`170
`3.8.3
`Vectorial View of MPSK Signaling,
`3.8.4 BPSK and QPSK Have the Same Bit Error
`Probability,
`171
`172
`Vectorial View of MFSK Signaling,
`3.8.5
`Symbol Error Performance for M—ary Systems (M > 2),
`3.9.]
`Probability of Symbol Error for MPSK,
`176
`3.9.2
`Probability of Symbol Error for MFSK,
`177
`3.9.3
`Bit Error Probability versus Symbol Error Probability
`for Orthogonal Signals,
`180
`Bit Error Probability versus Symbol Error Probability
`for Multiple Phase Signaling,
`181
`Effects of Intersymbol Interference,
`3.9.5
`3.10 Conclusion,
`182
`References,
`182
`Problems,
`183
`
`167
`
`176
`
`182
`
`i
`
`A
`
`,
`;
`.
`
`4 COMMUNICATIONS LINK ANALYSIS
`
`187
`
`4.1 What the System Link Budget Tells the System
`Engineer,
`188
`189
`The Channel,
`189
`4.2.1
`The Concept ofFree Space,
`4.2.2
`Signal-to-Noise Ratio Degradation,
`4.2.3
`Sources of Signal Loss and Noise,
`Received Signal Power and Noise Power,
`4.3.1
`The Range Equation,
`195
`4.3.2
`Received Signal Power as a Function of
`Frequency,
`199
`Path Loss Is Frequency Dependent,
`Thermal Noise Power,
`202
`
`4.2
`
`4.3
`
`4.3.3
`4.3.4
`
`I90
`190
`195
`
`200
`
`x
`
`Contents
`
`
`
`

`

`4,4
`
`4.6
`
`204
`Link Budget Analysis,
`205
`4.4.1
`Two Eb/No Values ofInterest,
`4.4.2
`Link Budgets Are Typically Calculated in
`Decibels,
`206
`4.4.3 How Much Link Margin Is Enough?,
`4.4.4
`Link Availability,
`209
`4.5 Noise Figure, Noise Temperature, and System
`Temperature,
`213
`213
`4.5.1 Noise Figure,
`4.5.2 Noise Temperature,
`4.5.3
`Line Loss,
`216
`4.5.4
`Composite Noise Figure and Composite Noise
`Temperature,
`218
`System Efi‘ective Temperature,
`4.5.5
`Sky Noise Temperature,
`224
`4.5.6
`Sample Link Analysis,
`228
`228
`4.6.]
`Link Budget Details,
`4.6.2
`Receiver Figure-of-Merit,
`4.6.3
`Recieved Isotropic Power,
`
`207
`
`215
`
`220
`
`230
`231
`
`4.7
`
`4.8
`4.9
`
`232
`Satellite Repeaters,
`232
`4.7.1 Nonregenerative Repeaters,
`4.7.2 Nonlinear Repeater Amplifiers,
`System Trade-Offs,
`238
`Conclusion,
`239
`References,
`239
`Problems,
`240
`
`236
`
`5 CHANNEL CODING: PART 1
`
`245
`
`246
`5.1 Waveform Coding,
`5.1.1 Antipodal and Orthogonal Signals,
`5.1.2
`M—ary Signaling,
`249
`5.1.3 Waveform Coding with Correlation Detection,
`5.1.4 Orthogonal Codes,
`251
`5.1.5
`Biorthogonal Codes,
`255
`5.1.6
`Transorthogonal (Simplex) Codes,
`Types of Error Control,
`258
`258
`5.2.1
`Terminal Connectivity,
`5.2.2 Automatic Repeat Request,
`Structured Sequences,
`260
`5.3.1
`Channel Models,
`261
`5.3.2
`Code Rate and Redundancy,
`5.3.3
`Parity-Check Codes,
`263
`5.3.4
`Coding Gain,
`266
`
`247
`
`257
`
`249
`
`259
`
`263
`
`5.2
`
`5 .3
`
`7
`
`is
`
`Contents
`
`xi
`
`
`
`

`

`270
`
`269
`Linear Block Codes,
`269
`5.4.1
`Vector Spaces,
`5.4.2
`Vector Subspaces,
`.
`A (6, 3) Linear Block Code Example,
`5.4.3
`5.4.4 Generator Matrix,
`272
`5.4.5
`Systematic Linear Block Codes,
`5.4.6
`Parity-Check Matrix,
`275
`5.4.7
`Syndrome Testing,
`276
`5.4.8
`Error Correction,
`277
`
`273
`
`271
`
`280
`Coding Strength,
`280
`5.5.1 Weight and Distance of Binary Vectors,
`5.5.2 Minimum Distance of a Linear Code,
`281
`5.5.3
`Error Detection and Correction,
`281
`5.5.4 Visualization of a 6-Tuple Space,
`285
`5.5.5
`Erasure Correction,
`287
`
`288
`Cyclic Codes,
`5 .6.1 Algebraic Structure of Cyclic Codes,
`5.6.2
`Binary Cyclic Code Properties,
`290
`5.6.3
`Encoding in Systematic Form,
`290
`5.6.4
`Circuit for Dividing Polynomials,
`292
`5.6.5
`Systematic Encoding with an (n — k)-Stage Shift
`Register,
`294
`Error Detection with an (n — k)-Stage Shift
`Register,
`296
`
`5.6.6
`
`288
`
`5.4
`
`5.5
`
`5.6
`
`
`
`I I
`
`‘
`
`E i
`
`5.7 Well-Known Block Codes,
`5 .7.1 Hamming Codes,
`298
`5.7.2
`Extended Golay Code,
`5.7.3 BCH Codes,
`301
`5.7.4
`Reed—Solomon Codes,
`Conclusion,
`308
`References,
`308
`Problems,
`309
`
`5.8
`
`298
`
`301
`
`304
`
`6 CHANNEL CODING: PART 2
`
`314
`
`6.1
`6.2
`
`6.3
`
`xii
`
`315
`Convolutional Encoding,
`Convolutional Encoder Representation,
`6.2.1
`Connection Representation,
`318
`6.2.2
`State Representation and the State Diagram,
`6.2.3
`The Tree Diagram,
`324
`6.2.4
`The Trellis Diagram,
`326
`Formulation of the Convolutional Decoding Problem,
`6.3.] Maximum Likelihood Decoding,
`327
`6.3.2
`Channel Models: Hard versus Soft Decisions,
`6.3.3
`The Viterbi Convolutional Decoding Algorithm,
`
`317
`
`322
`
`327
`
`329
`333
`
`Contents
`
`

`

`397
`Bandwidth—Limited Systems,
`7.7
`397
`7.8 Modulation and Coding Trade-Offs,
`399
`7.9
`Bandwidth-Efficient Modulations,
`7.9.1
`QPSK and Offset QPSK Signaling.
`7.9.2 Minimum Shift Keying,
`403
`407
`7.9.3 Quadrature Amplitude Modulation,
`7.10 Modulation and Coding for Bandlimited Channels,
`7.10.] Commercial Telephone Modems,
`411
`7.10.2 Signal Constellation Boundaries,
`412
`7.10.3 Higher-Dimensional Signal Constellations,
`7.10.4 Higher-Density Lattice Structures,
`415
`7.10.5 Combined-Gain: N-Sphere Mapping and Dense
`Lattice,
`416
`7.10.6 Trellis—Coded Modulation,
`7.10.7 Trellis-Coding Example,
`7.11 Conclusion,
`424
`References,
`425
`Problems,
`426
`
`399
`
`410
`
`412
`
`'
`
`.
`
`'
`l
`I
`3
`3
`
`417
`420
`
`8 SYNCHRONIZATION
`Maurice A. King, Ir.
`
`429
`
`8.]
`
`8.2
`
`Synchronization in the Context of Digital
`Communications,
`430
`430
`8.1.1 What It Means to Be Synchronized,
`8.1.2
`Costs versus Benefits of Synchronization
`Levels,
`432
`434
`Receiver Synchronization,
`8.2.1
`Coherent Systems: Phase-Locked Loops,
`8.2.2
`Symbol Synchronization,
`453
`8.2.3
`Frame Synchronization,
`460
`8.3 Network Synchronization,
`464
`8.3 .1
`Open—Loop Transmitter Synchronization,
`8.3.2
`Closed-Loop Transmitter Synchronization,
`Conclusion,
`470
`References,
`471
`Problems,
`472
`
`8.4
`
`434
`
`465
`468
`
`g MULTIPLEXING AND MULTIPLE ACCESS
`
`475
`
`9.1
`
`Allocation of the Communciations Resource,
`9.1.1
`Frequency—Division Multiplexing/Multiple
`Access,
`478
`
`476
`
`xiv
`
`Contents
`
`
`
`

`

`9.1.2
`9.1.3
`9.1.4
`
`9.1.5
`9.1.6
`
`484
`487
`
`Time-Division Multiplexing/Multiple Access,
`Communications Resource Channelization,
`Performance Comparison of FDMA and
`TDMA,
`488
`491
`Code-Division Multiple Access,
`Space-Division and Polarization-Division Multiple
`Access,
`493
`9.2 Multiple Access Communications System and
`Architecture,
`495
`496
`9.2.1 Multiple Access Information Flow,
`9.2.2 Demand-Assignment Multiple Access, 497
`9.3 Access Algorithms,
`498
`9.3.1 ALOHA,
`498
`500
`9.3.2
`Slotted ALOHA,
`502
`9.3.3
`Reservation-ALOHA,
`9.3.4
`Performance Comparison of S-ALOHA
`and R-ALOHA,
`503
`
`505
`Polling Techniques,
`9.3.5
`9.4 Multiple Access Techniques Employed with -
`INTELSAT,
`507
`9.4.1
`Preassigned FDM/FM/FDMA 0r MCPC
`Operation,
`508
`9.4.2 MCPC Modes ofAccessing an INTELSAT
`Satellite,
`510
`SPADE Operation, 51]
`9.4.3
`TDMA in INTELSAT,
`516
`9.4.4
`523
`Satellite-Switched TDMA in INTELSAT,
`9.4.5
`9.5 Multiple Access Techniques for Local Area Networks,
`9.5.1
`Carrier-Sense Multiple Access Networks,
`526
`9.5.2
`Token-Ring Networks,
`528
`9.5.3
`Performance Comparison of CSMA/CD
`and Token-Ring Networks,
`530
`Conclusion,
`531
`References,
`532
`Problems,
`533
`
`9.6
`
`526
`
`‘10 SPREAD=SPECTRUM TECHNIQUES
`
`10.1
`
`537
`Spread-Spectrum Overview,
`10.1.1
`The Beneficial Attributes of Spread-Spectrum
`Systems,
`538
`10.1.2 Model for Spread-Spectrum Interference
`Rejection,
`542
`A Catalog of Spreading Techniques,
`10.1.3
`10.1.4 Historical Background,
`544
`
`543
`
`Contents
`
`XV
`
`536
`
`

`

`10.2
`
`10.3
`
`10.4
`
`10.5
`
`10.6
`
`10.7
`
`10.8
`
`549
`
`557
`
`552
`
`559
`560
`
`546
`Pseudonoise Sequences,
`546
`10.2.1
`Randomness Properties,
`547
`10.2.2
`Shift Register Sequences,
`548
`10.2.3
`PN Autocorrelation Function,
`Direct-Sequence Spread-Spectrum Systems,
`10.3.1
`Example ofDirect Sequencing,
`550
`10.3.2
`Processing Gain and Performance,
`Frequency Hopping Systems,
`555
`10.4.1
`Frequency Hopping Example,
`10.4.2
`Robustness,
`558
`10.4.3
`Frequency Hopping with Diversity,
`10.4.4
`Fast Hopping versus Slow Hopping,
`10.4.5
`FFH/MFSK Demodulator,
`562
`Synchronization,
`562
`10.5.1 Acquisition.
`563
`10.5.2
`Tracking,
`568
`571
`Spread-Spectrum Applications,
`10.6.1
`Code-Division Multiple Access,
`10.6.2 Multipath Channels,
`573
`10.6.3
`The Jamming Game,
`574
`Further Jamming Considerations,
`10.7.1 Broadband Noise Jamming,
`10.7.2
`Partial-Band Noise Jamming,
`10.7.3 Multiple-Tone Jamming,
`583
`10.7.4
`Pulse Jamming,
`584
`10.7.5 Repeat-Back Jamming,
`10.7.6
`BLADES System,
`588
`Conclusion,
`589
`References,
`589
`Problems,
`591
`
`571
`
`579
`579
`581
`
`586
`
`SOURCE CODING
`
`Fredric I. Harris
`
`595
`
`596
`601
`
`596
`Sources,
`11.1.1
`Discrete Sources,
`11.1.2 Waveform Sources,
`Amplitude Quantizing,
`603
`11.2.1
`Quantizing Noise,
`605
`11.2.2
`Uniform Quantizing,
`608
`11.2.3
`Saturation,
`611
`11.2.4 Dithering,
`614
`617
`11.2.5
`Nonuniform Quantizing,
`Differential Pulse Code Modulation,
`11.3.1
`One-Tap Prediction,
`630
`11.3.2
`N—Tap Prediction,
`631
`
`627
`
`11.1
`
`11.2
`
`11.3
`
`xvi
`
`Contents
`
` 11
`
`

`

`11.4
`
`11.5
`
`11.6
`
`11.7
`
`11.3.3 Delta Modulation,
`
`633
`
`639
`
`11.3.4 Adaptive Prediction,
`Block Coding,
`643
`643
`11.4.1
`Vector Quantizing,
`645
`11.4.2
`Transform Coding,
`11.4.3
`Quantization for Transform Coding,
`11.4.4
`Subband Coding,
`647
`
`647
`
`Synthesis/Analysis Coding,
`11.5.1
`Vocoders,
`650
`11.5.2
`Linear Predictive Coding,
`
`649
`
`Redundancy-Reducing Coding,
`11.6.1
`Properties ofCodes,
`655
`11.6.2
`Hufiman Code,
`657
`11.6.3
`Run-Length Codes,
`Conclusion,
`663
`References,
`663
`Problems,
`664
`
`660
`
`653
`
`653
`
`_.
`-"
`'
`
`12 ENCRYPTION AND DECRYPTION
`
`668
`
`669
`12.1 Models, Goals, and Early Cipher Systems,
`12.1.1
`A Model of the Encryption and Decryption
`Process,
`669
`System Goals,
`Classic Threats,
`Classic Ciphers,
`
`12.1.2
`12.1.3
`12.1.4
`
`671
`671
`672
`
`12.2
`
`12.3
`
`12.4
`
`The Secrecy of a Cipher System,
`12.2.1
`Perfect Secrecy,
`675
`678
`12.2.2
`Entropy and Equivocation,
`12.2.3
`Rate ofa Language and Redundancy,
`12.2.4
`Unicity Distance and Ideal Secrecy,
`Practical Security,
`683
`12.3.1
`Confusion and Diffusion,
`12.3.2
`Substitution,
`683
`12.3.3
`Permutation,
`685
`686
`12.3.4
`Product Cipher System,
`12.3.5
`The Data Encryption Standard,
`
`675
`
`683
`
`680
`680
`
`687
`
`12.4.2
`
`694
`Stream Encryption,
`12.4.1
`Example of Key Generation Using a Linear
`Feedback Shift Register,
`694
`Vulnerabilities of Linear Feedback Shift
`Registers,
`695
`Synchronous and SelfSynchronous Stream
`Encryption Systems,
`697
`
`12.4.3
`
`Contents
`
`xvii
`
`
`
`

`

`.1
`
`.4”.
`
`E i
`
`,i
`
`:
`
`%
`
`12.5
`
`12.6
`
`12.5.2
`12.5.3
`12.5.4
`12.5.5
`
`698
`Public Key Cryptosystems,
`12.5 .1
`Signature Authentication Using a Public Key
`Cryptosystem,
`699
`700
`A Trapdoor One-Way Function,
`The Rivest—Shamir—Adelman Scheme,
`The Knapsack Problem,
`703
`A Public Key Cryptosystem Based on a Trapdoor
`Knapsack,
`705
`Conclusion,
`707
`References,
`707
`Problems,
`708
`
`70]
`
`A A REVIEW OF FOURIER TECHNIQUES
`
`710
`
`A]
`A2
`
`A.3
`
`A.4
`
`A.5
`
`A.6
`
`710
`Signals, Spectra, and Linear Systems,
`Fourier Techniques for Linear System Analysis,
`A.2.1
`Fourier Series Transform,
`713
`A.2.2
`Spectrum of a Pulse Train,
`716
`A.2.3
`Fourier Integral Transform,
`719
`Fourier Transform Properties,
`720
`A.3.1
`Time Shifting Property,
`720
`A.3 .2
`Frequency Shifting Property,
`Useful Functions,
`721
`A.4.1
`Unit Impulse Function,
`A.4.2
`Spectrum of a Sinusoid,
`Convolution,
`722
`A5 .1
`Graphical Illustration of Convolution,
`A5 .2
`Time Convolution Property,
`726
`726
`A5.3
`Frequency Convolution Property,
`A.5 .4
`Convolution of a Function with a Unit
`Impulse,
`728
`- Demodulation Application of Convolution,
`A5 .5
`Tables of Fourier Transforms and Operations,
`References,
`732
`
`720
`
`72]
`721
`
`711
`
`729
`731
`
`726
`
`
`
`B FUNDAMENTALS OF STATISTICAL DECISION
`THEORY
`
`733
`Bayes’ Theorem,
`3.1 .1
`Discrete Form of Bayes’ Theorem,
`3.1.2 Mixed Farm of Bayes’ Theorem,
`
`734
`736
`
`B.1
`
`xvlii
`
`733
`
`Contents
`
`
`
`

`

`32
`
`3.?»
`
`738
`Decision Theory,
`3.2.1
`Components of the Decision Theory Problem,
`3.2.2
`The Likelihood Ratio Test and the Maximum
`A Posteriori Criterion,
`739
`3.2.3
`The Maximum Likelihood Criterion,
`Signal Detection Example,
`740
`3.3.1
`The Maximum Likelihood Binary Decision,
`B3 .2
`Probability of Bit Error,
`741
`References ,
`743
`
`739
`
`740
`
`738
`
`RESPONSE OF CORRELATORS TO WHITE NOISE
`
`OFTEN USED IDENTITIES
`
`A CONVOLUTIONAL ENCODER/DECODER
`COMPUTER PROGRAM
`
`LIST OF SYMBOLS
`
`INDEX
`
`Contents
`
`744
`
`746
`
`748
`
`759
`
`765
`
`xix
`
`

`

`fig
`
`CEL‘iPTER 1
`
`Signals and Spectra
`
`From other
`sources
`
`
`
`r _ _ ._
`Source
`
`Channel
`
`_____________ ._ _[
`1
`i
`
`Information
`source
`
`llIl
`
`Digital
`input
`mi
`
`
`
`Bit
`Synch-
`stream
`Digital
`
`ronization I waveform
`
`
`
`Information
`sink
`
`Channel
`bits
`
`__
`
`
`|
`l
`_ _ _ _ _ _ _ _ _l
`
`Optional
`
`D Essential
`
`1
`
`To other
`destinations
`
`
`
`:
`}|
`I
`
`
`
`Digital
`
`

`

`1.1 DI
`
`offs among basic system parameters such as signal-to—noise ratio (SNR), proba-
`
`
`
`

`

`from an infinite variety of waveform shapes with theoretically infinite resolution.
`In a DCS, the objective at the receiver is not to reproduce a transmitted waveform
`with precision; it is, instead, to determine from a noise-perturbed signal which
`waveform from the finite set of waveforms had been sent by the transmitter. An
`important measure of system performance in a DCS is the probability of error
`(PE)-
`
`1 1 DlGITAL COMMUNICATION SIGNAL PROCESSING
`
`1.1.1 Why Digital?
`
`3
`
`Why are communication systems, military and commercial alike, “going digital” ?
`There are many reasons. The primary advantage is the ease with which digital
`signals, compared to analog signals, are regenerated. Figure 1.1 illustrates an ideal
`binary digital pulse propagating along a transmission line. The shape of the wave-
`form is affected by two basic mechanisms: (1) as all transmission lines and circuits
`have some nonideal transfer function, there is a distorting effect on the ideal pulse;
`and (2) unwanted electrical noise or other interference further distorts the pulse
`waveform. Both of these mechanisms cause the pulse shape to degrade as a func-
`tion of line length, as shown in Figure 1.1. During the time that the transmitted
`pulse can still be reliably identified (before it is degraded to an ambiguous state
`by the transmission line), the pulse is amplified by a digital amplifier that recovers
`its original ideal shape. The pulse is thus “reborn” or regenerated. Circuits that
`perform this function at regular intervals along a transmission system are called
`regenerative repeaters.
`Digital circuits are less subject to distortion and interference than are analog
`circuits. Since binary digital circuits operate in one of two states, fully on or fully
`off, to be meaningful a disturbance must be large enough to change the circuit
`operating point from one state to the other. Such two-state operation facilitates
`signal regeneration and thus prevents noise and other disturbances from accu-
`
`Distance 1
`
`Original
`pulse signal
`
`Distance 2
`
`Some signal
`distortion
`
`Distance 3
`
`Distance 4
`
`Distance 5
`
`Degraded
`signal
`
`Signal is badly
`degraded
`
`Amplification
`to regenerate
`pulse
`
`I
`
`1
`2
`3
`4
`5
`
`Propagation distance—>
`
`Figure 1.1 Pulse degradation and regeneration.
`
`Sec. 1.1
`
`Digital Communication Signal Processing
`
`,
`
`'
`
`'
`
`

`

`mulating in transmission, Analog signals, however, are not two-state signals; they
`can take an infinite variety of shapes. With analog circuits, even a small disturb—
`ance can render the reproduced waveform unacceptably distorted. Once the an-
`alog signal is distorted, the distortion cannot be removed by amplification. Since,
`with analog signals, accumulated noise is irrevocably bound to the signal, analog
`signals cannot be completely regenerated. Extremely low error rates producing
`high signal fidelity are possible through error detection and correction with digital
`techniques, but similar procedures are not available with analog.
`There are other important advantages to digital communications. Digital
`circuits are more reliable and can be produced at lower cost than analog circuits.
`Also, digital hardware lends itself to more flexible implementation than analog
`hardware [e.g., microprocessors, digital switching, and large-scale integrated
`(LSI) circuits]. The combining of digital signals using time-division multiplexing
`(TDM) is simpler than the combining of analog signals using frequency-division
`multiplexing (FDM). Different types of digital signals (data, telegraph, telephone,
`television) can be treated as identical signals in transmission and switching—a
`bit is a bit. Also, for convenient switching, digital messages can be handled in
`autonomous groups called packets. Digital techniques lend themselves naturally
`to signal processing functions that protect against interference and jamming, or
`that provide encryption and privacy; such techniques are discussed in Chapters
`10 and 12, respectively. Also, much data communication is computer to computer,
`or digital instrument or terminal to computer. Such digital teraninations are nat-
`urally best served by digital communication links.
`Most system choices entail trade-offs; system options are rarely all good or
`all bad. Thus far we have discussed only the benefits of digital transmission. What
`do you suppose are the costs or liabilities? A major disadvantage of digital trans-
`mission is that it typically requires a greater system bandwidth to communicate
`the same information in a digital format as compared to an analog format. Through-
`out this book we emphasize that bandwidth is a valuable resource, not always
`available. Bandwidth-efficient signaling techniques are discussed in Chapters 2
`and 7. Another cost of digital transmission is that digital detection requires system
`synchronization (Chapter 8), whereas analog signals generally have no such
`requirement.
`
`1.1.2 Typical Block Diagram and Transformations
`
`The functional block diagram shown in Figure 1.2 illustrates the signal flow
`through a typical DCS. The upper blocks—format, source encode, encrypt, chan-
`nel encode, multiplex, modulate, frequency spread, and multiple access—indicate
`the signal transformations from the source to the transmitter. The lower blocks
`indicate the signal transformations from the receiver to the sink; the lower blocks
`essentially reverse the signal processing steps performed by the upper blocks. It
`used to be that the only blocks within the dashed lines were the modulator and
`demodulator, together called a modem. During the past two decades, other signal
`processing functions were frequently incorporated within the same assembly as
`the modulator and demodulator. Consequently, at present, the term “modem”
`
`4
`
`Signals and Spectra
`
`Chap. 1
`
`
`
`
`
`

`

`
`
`T
`
`a
`n
`n
`
`R
`C
`
`Information (cid:9)
`source (cid:9)
`
`T - - - - (cid:9)
`Source (cid:9)
`i (cid:9)
`bits (cid:9)
`I (cid:9)
`
`--------
`Channel
`bits
`
`From other
`sources
`
`4Fr’
`
`Format (cid:9)
`
`YJi99 (cid:9)
`
`Ir
`1J Mod-
`sj(t) /7
`:ut\
`
`Mult(
`
`I (cid:9)
`Freq- j 4Multi Ple4
`uenCy (cid:9)
`
`es acc s
`late Ispread (cid:9)
`Irr/ZZr/-Zz (cid:9)
`
`I
`
`I
`
`Bit (cid:9)
`stream
`
`ronization
`
`Digital (cid:9)
`waveform (cid:9)
`
`I (cid:9)
`I (cid:9)
`
`Digital
`input
`M i
`
`
`
`Digital (cid:9)Digital
`
`o_*] Format
`
`[.4-
`
`: (cid:9)
`
`DecryptJ-4- decode (cid:9)
`plex (cid:9)
`ulate (cid:9)
`IChannflemuItflemod-I (cid:9)
`
`t
`Information (cid:9)
`sink (cid:9)
`
`It (cid:9)
`I Source (cid:9)
`bits (cid:9)
`L ___
`
`Optional (cid:9)
`
`Essential (cid:9)
`
`Freq- (cid:9)
`
`Multiple
`access
`
`--J
`
`-
`
`It
`
`Channel
`bits
`__
`
`To other
`destinations
`
`Figure 1.2 Block diagram of a typical digital communication system. (Reprinted with per-
`mission from B. Sklar, "A Structured Overview of Digital Communications," IEEE Corn-
`mun. Mag., August 1983, Fig. 1, p. 5. ' 1983 IEEE.)
`
`often encompasses all the processing steps shown within the dashed lines of Figure
`1.2; when this is the case, the modem can be thought of as the "brains" of the
`system. Note that what constitutes a modem is not a precise concept; some of
`the blocks have purposely been shown on the dashed line rather than either inside
`or outside the modem. The transmitter and receiver can be thought of as the
`"muscles" of the system. The transmitter usually consists of a frequency up-
`conversion stage, a high-power amplifier, and an antenna. The receiver portion
`usually consists of an antenna, a low-noise amplifier (LNA), and a down-converter
`stage, typically to an intermediate frequency (IF).
`Of all the signal processing steps, only formatting, modulation, and demod-
`ulation are essential for a DCS; the other processing steps within the modem are
`design options for specific system needs. Formatting transforms the source in-
`formation into digital symbols; it makes the information compatible with the signal
`processing within a digital communication system. Modulation is the process by
`which the symbols are converted to waveforms that are compatible with the trans-
`mission channel.
`The source encoding step produces analog-to-digital (AID) conversion (for
`
`Sec. 1.1 (cid:9)
`
`Digital Communication Signal Processing (cid:9)
`
`5
`
`

`

`analog sources) and removes redundant 0r unneeded information. Encryption
`prevents unauthorized users from understanding messages and from injecting false
`messages into the system. Channel coding, for a given data rate, can reduce the
`probability of error (PE), or reduce the signal-to-noise ratio (SNR) requirement,
`at the expense of bandwidth or decoder complexity. Channel coding can also
`reduce the system bandwidth requirement at the expense of SNR or PE' perform-
`ance. Frequency spreading can produce a signal that is less vulnerable to inter-
`ference (both natural and intentional) and can be used to enhance the privacy of
`the communicators. Multiplexing and multiple access procedures combine signals
`that might have different characteristics or might originate from different sources,
`so that they can share a portion of the communications resource.
`The flow of the signal processing steps shown in Figure 1.2 represents a
`typical arrangement; however, the blocks are sometimes implemented in a dif-
`ferent order. For example, multiplexing can take place prior to channel encoding,
`or prior to modulation, or—with a two-step modulation process (subcarrier and
`carrier)—it can be performed between the two modulation steps. Similarly,
`spreading can take place anywhere along the transmission chain; its precise lo-
`cation depends on the particular technique used. Figure 1.2 illustrates the recip-
`rocal aspect of the procedure; any signal processing step that takes place in the
`transmitting chain must be reversed in the receiving chain. The figure also indi-
`cates that from the source to the modulator a message, also called a baseband
`signal or a bit stream, is characterized by a sequence of digital symbols. After
`modulation, the message takes the form of a digitally encoded waveform or digital
`waveform. Similarly, in the reverse direction, a received message appears as a
`digital waveform until it is demodulated. Thereafter it takes the form of a bit
`stream for all further signal processing steps. At various points along the signal
`route, noise corrupts the waveform s(t) so that its reception must be termed an
`estimate s‘(t). Such noise and its deleterious effects on system performance are
`considered in Chapter 4.
`Figure 1.3 shows the basic signal processing functions, which may be vieWed
`as transformations from one signal space to another. The transformations are
`classified into seven basic groups:
`
`Formatting and source coding
`. Modulation/demodulation
`
`flail-gaggle;—
`
`Channel coding
`
`Multiplexing and multiple access
`. Spreading
`
`. Encryption
`
`Synchronization
`
`Although this organization has some inherent overlap, it provides a useful
`structure for the book. Beginning with Chapter 2, the seven basic transformations
`are considered individually. In Chapter 2 we discuss the basic formatting tech-
`niques for transforming the source information into digital symbols, as well as
`
`6
`
`Signals and Spectra
`
`Chap. 1
`
`

`

`Formatting/Source Coding
`
`Bandpass Modulation/Demodulat ion
`
`Coherent
`
`Noncoherent
`
` Differential PCM (DPCM)
` character coding
`
`Sampling
`_
`Quantization
`Pulse code modulation (PCM)
`
`
`
`Block coding
`Synthesis/analysis coding
`Redundancy reducing coding
`
`
`
`Phase shift
`keying (PSK)
`Frequency shift
`keying (FSK)
`Amplitude shift
`keying (ASK)
`Continuous phase
`modulation
`(CPM)
`
`Differential phase
`shift keying
`(DPSK)
`Frequency shift
`keying (FSK)
`Amplitude shift
`keying (ASK)
`Continuous phase
`modulation
`(CPM)
`Hybrids
`
`Hybrids
`(PUMA)
`Data stream
`
`Channel Coding
`
`Waveform
`
`Structured
`Sequences
`
`Antipodal
`
` IVI-ary signaling
`
`Orthogonal
`Biorthogonal
`Transorthogonal
`
`Convolutional
`
`Spreading
`
`Direct sequencing
`(DS)
`Frequency hopping
`(FH)
`Time h0pping (TH)
`Hybrids
`
`\
`
`'--
`
`Synchronization
`
`Multiplexing/Multiple Access
`
`Frequency division
`
`(FDM/FDMA)
`
`Time division
`(TDM/TDMA)
`Code division
`(CD M/CD MA)
`Space division
`(SDMA)
`Polarization division
`
`Carrier
`
`synchronization
`
`Subcarrier
`synchronization
`Symbol
`synchronization
`Frame
`synchronization
`Network
`synchronization
`
`Encryption
`
`' Block
`
`V
`
`Figure 1.3 Basic digital communication transformations. (Reprinted with permission from
`B. Sklar, “A Structured Overview of Digital Communications,” IEEE Commun. Mag.,
`August 1983, Fig. 2, p. 6. © 1983 IEEE.)
`
`the selection of waveforms for making the symbols compatible with baseband
`transmission. As seen in Figure 1.3, formatting and source coding are grouped
`together; they are similar in that they involve data digitization. Since the term
`“source coding” has taken on the connotation of data redundancy reduction in
`addition to digitization, it is treated later, as a special formatting case, in Chapter
`11.
`
`In Figure 1.3, bandpass modulation/demodulation is partitioned into two
`basic categories, coherent and noncoherent. The process of demodulation in-
`volves the detection of the baseband information. Digital demodulation is typically
`accomplished with the aid of reference waveforms. When the references contain
`all the signal attributes, particularly phase information, the process is termed
`coherent; when phase information is not used, the process is termed noncoherent.
`Both techniques are detailed in Chapter 3.
`'
`
`Sec. 1.1
`
`Digital Communication Signal Processing
`
`7
`
`
`
`

`

`‘
`
`!,
`
`K
`
`(pulse amplitude modulation). Do you suppose that the sampled data in Figure
`
`2.14bare compatible with adigital system? No, they are not, because the am—
`
`plitude of each natural sample still has an infinite number of possible values; a
`digital system deals with a finite number of symbols. Even if the sampling is flat-
`top sampling, thepossible pulse values form an infinite set, since they reflect all
`the possible values of the continuous analog waveform. Figure 2.14c illustrates
`the original waveform represented by discrete pulses. Here the pulses have flat
`tops and the pulse amplitude values are limited to a finite set. Each pulse is
`expressed as a level from a finite number of predetermined levels; each such level
`can be represented by a symbol from a finite alphabet. The pulses in Figure 2.14c
`are referred to as quantized samples; such a format is the obvious choice for
`
`interfacing with a digital system. The format in Figure 2.14d may be construed
`as the output of a sample-and—hold circuit. When the sample values are quantized
`to a finite set, this format can also interface with a digital system. After quanti-
`zation, the analog waveform can still be recovered, but not precisely; improved
`reconstruction fidelity of the analog waveform can be achieved by increasing the
`number of quantization levels (requiring increased syste