11.4 Decoding Technique
`
`267
`
`==
`
`Leety
`
`ia
`=|
`
`& a
`
` o>
`Fig. 11.13 Microtune single-chip tuner integrated onto the receiver's main PCB :
`
`The advantages of this concept are the following: Tuners are small, light-
`weight, are integratedin the receiver board andare cost-effective in produc-
`tion (no manual placementandsoldering of the tuner module). On the other
`hand the disadvantages in comparison to the moreclassical tuners are the
`following: the powerdissipation is higher, the performanceis slightly lower
`and the cost of the componentsis higher - butthis list is from a snapshot
`from the year 2002, and timewilltell.
`
`11.4.3.3 Network Interface Module (NIM) Technology
`Oneinteresting technology for reception of DVB signals in general — beit
`DVB-S, DVB-C, or DVB-T-is the implementation of a so-called Network In-
`terface Module (NIM). A NIM integrates in one moduleall signal processing
`from RF input to MPEG-2 transport stream output, containing the whole
`tuner functionality, the IF processing and the DVB-T decoderchip. Thesize
`of such a module is roughly double that of a standard tuner module. A NIM
`shows a numberofadvantagesfor the design of a DVB-Treceiver:
`
`-
`
`It has all the technology and the related know-how "on board“, in one
`module. The receiver designer is therefore not forced to bother with RF
`problemslike crosstalk etc.
`- The RF andIF processing and also the PCB layout are optimally adapted
`to the integrated DVB-T decoderchip.
`- NIMsfrom various manufactures can be interchanged. The controlsoft-
`ware needs to be adapted, which usually is rather simply done by re-
`
`3 With kind permission of Microtune (TEMIC-Tuners)
`
`96
`
` [
`
`py
`
`96
`
`

`

`
`
`268
`
`_11 The StandardforTerrestrial Transmission andIts Decoding Technique
`
`designing the appropriate IC bus routines, and a rather small PCB layout
`adaptationis typically required.
`Since NIMsare available for the different DVB transmission systems,
`namelysatellite, cable, and terrestrial, they allow for the design ofexactly
`the samereceiverfor all DVB systems.
`Figure 11.14 showsa typical Network Interface Module (NIM).
`
`
`
`Fig. 11.14 NetworkInterface Module (NIM) from Philips - (top and bottom view)
`
`Integrating a NIM intoareceiver design is a good choice for PC-extension
`cards and for set-top boxes. If the performance is good enough they may be
`used for IDTVs. But for other receiver classes, NIMs have two main disad-
`vantages:
`Thesize of a NIM isstill quite large, so it may not be the perfect choice
`for small receiverslike in PDAs or USB card extensions.
`It is not possible to control the RF- and IF-processing parameters, espe-
`cially of the AGC from outside the NIM. These parameters, therefore,
`cannotbe adaptedor optimisedfordifficult reception conditionslike the
`mobile channel. The NIMis thereforeless suited for automotive receivers
`and PDAs
`The designer has to take the module "asit is“. Exchangingparts,e.g. the
`DVB-T decoderor the SAWfilter, for a part from a different supplieris
`not possible.
`
`
`* With kind permissionofPhilips Components, Business Unit Tuners.
`
`97
`
`97
`
`

`

`APPENDIX (cid:56)(cid:49)(cid:39)(cid:40)(cid:53)(cid:37)(cid:50)(cid:40)(cid:38)(cid:46)(cid:19)(cid:20)
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`APPENDIX (cid:53)(cid:50)(cid:37)(cid:44)(cid:49)(cid:54)(cid:19)(cid:20)
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`APPENDIX (cid:51)(cid:53)(cid:50)(cid:36)(cid:46)(cid:44)(cid:54)(cid:19)(cid:20)
`APPENDIX PROAKISO1
`
`108
`
`108
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`

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`
`Pecls
`fr) UniversityLibraries Catalog
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`LIAS2438679 a
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`(OCoLC)43526842
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`00 1
`Proakis,John G.
`
`245 1
`Digital communications / c|John G. Proakis.
`=#0
`
`250
`a| 4th ed., International ed.
`
`264
`1
`al Boston: b| McGraw-Hill, ¢| [2001]
`264
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`
`300
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`xxi, 1,002 pages : b| illustrations; ¢| 24 cm.
`
`336
`a|
`text b|
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`338
`a| volume b| nc 2| rdacarrier
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`490 1
`a| McGraw-Hill series in electrical and computer engineering
`
`al Includesbibliographical references (pages 963-992) and index.
`504
`
`650
`0 a| Digital communications.
`;
`830
`a| McGraw-Hill series in electrical and computer engineering.
`949
`a| TK5103.7.P76 2001 w| LC c¢|
`1
`i] 000054535237 d| 4/24/2013 e| 1/7/2013 || STACKS-HB3 m| HARRISBURG n| 26r| ¥Ys| ¥ t|
`BOOKFLOATu| 3/15/2005
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`
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`aa 1
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`UNIVERSITY LIBRARIES.
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`PENN STATE oe)bioe suye
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`UNIVERSITY LIBRARIES
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`Digital communications / John G. Proakis
`Author:
`Proakis, John G
`Published:
`Boston : McGraw-Hill, [2001]
`Copyright Date:
`©2001
`Edition:
`4th ed., International ed.
`Physical Description:
`xxi, 1,002 pages: illustrations ; 24 cm.
`
`Availability
`
`Alaa
`
`PennState Harrisburg (1 item)
`Call number
`
`TK5103.7.P76 2001
`
`Material
`
`Book
`
`Location
`
`Checkedout, request through Interlibrary Loan
`
`Series:
`
`McGraw-Hill series in electrical and computer engineering
`Subject(s):
`Digital communications
`
`ISBN:
`
`0072321113
`0071181830
`9780071181839
`
`Bibliography Note:
`Includesbibliographical references (pages 963-992) and index.
`
`PennState
`
`PENN STATE
`UNIVERSITY LIBRARIES.
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`PENN STATE
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`APPENDIX (cid:51)(cid:53)(cid:50)(cid:36)(cid:46)(cid:44)(cid:54)(cid:19)(cid:21)
`APPENDIX PROAKIS02
`
`111
`
`

`

`y
`he
`igi
`ooer
`omSnae
`
`o—===Sos
`
`fan=©oe
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`pra
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`
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`
`112
`
`

`

`A classic in the field, Digital Communications makes an excellent gr’
`
`Complete and thoroughintroduction to the analysis and design of digital communication
`systems that makes this book a must-have reference.
`
`Flexible organization that makes it useful in a one- or two-semester course.
`
`
`
`This new edition of John Proakis’ best-selling Digital Communications is up to date with new
`coverage of current trends in the field. New topics have been added that include:
`
`poTIiUNIVERSITYLIBRARIES= a
`! | | | l
`
`
`
`
`
`~—
`comprehensive reference book for the professional. This new edition maintait|AU00054535237
`earlier editions with its comprehensive coverage, accuracy, and excellent explanations.
`It
`
`continues to provide the flexible organization that makes this book a goodfit for a one- or two-
`semester course. Features of the new edition of Digital Communications include:
`
`
`
`
`
`
`
`
`
`
`Excellent end-of-chapter problems that challenge the student.
`An accompanying website that provides presentation material and solutions for
`instructors,
`#
`
`ISBN O-O?-e3e2111-3
`
`
`
`A Division of The McGraw-Hill Companies
`
`9 "780072"321111
`
`www.»mhhe.-com
`
`113
`
`Serial and Parallel Concatenated Codes
`Punctured Convolutional Codes
`Turbo TCM
`Turbo Equalization
`Spatial Multiplexing
`Digital Cellular CDMA System Based on DS Spread Spectrum
`Reduced Complexity ML Detectors
`
`‘
`
`
`
` McGraw-Hill Higher Education 3
`
`|
`|
`90000
`
`113
`
`

`

`
`
`Digital Communications
`
`Fourth Edition
`
`JOHN G. PROAKIS
`DepartmentofElectrical and Computer Engineering
`Northeastern University
`
`
`
`Boston Burr Ridge, IL Dubuque, IA Madison, WI New York San Francisco St. Louis
`Bangkok Bogota Caracas Lisbon London Madrid Mexico City Milan
`New Delhi Seoul Singapore
`Sydney Taipei Toronto
`
`114
`
`114
`
`

`

`
`
`McGraw-Hill Higher Education 5
`
`A Division of The McGraw-Hill Companies
`
`DIGITAL COMMUNICATIONS
`Published by McGraw-Hill, an imprint of The McGraw-Hill Companies, Inc., 1221 Avenue of
`the Americas. New York, NY, 10020. Copyright ©2001, 1995, 1989, 1983, by The McGraw-
`Hill Companies, Inc. All rights reserved. No part of this publication may be reproduced or
`distributed in any form or by any means, orstored in a database or retrieval system, without the
`prior written consent of The McGraw-Hill Companies, Inc., including, but notlimited to, in any
`network or other electronic storage or transmission, or broadcast for distance learning.
`Someancillaries, including electronic and print components, maynot be available to customers
`outside the United States.
`
`This book is printed on acid-free paper.
`
`4567890 DOC/DOC 09876543
`
`ISBN 0-07-2321 11-3
`
`Publisher: Thomas Casson
`Sponsoringeditor: Catherine Fields Shultz
`Developmental editor: Emily J. Gray
`Marketing manager: John Wannemacher
`Project manager: Craig S. Leonard
`Production supervisor: Rose Hepburn
`Senior designer: Kiera Cunningham
`Newmedia: Christopher Styles
`Compositor: Interactive Composition Corporation
`Typeface: /0.5/12 Times Roman
`Printer: Quebecor World Fairfield, Inc.
`
`Library of Congress Cataloging-in-Publication Data
`Proakis, John G.
`Digital communications / John G. Proakis.—4th ed.
`p. cm.
`ISBN 0-07-232111-3
`1. Digital communications. |. Title.
`TK5103.7.P76 2000
`621.382-de21
`
`00-025305
`
`www.mhhe.com
`
`115
`
`

`

`
`
`116
`
`

`

`[|CONTENTs
`
`Preface
`
`xix
`
`1
`
`l
`Introduction
`l
`1.1 Elements of a Digital Communication System
`3
`1.2 Communication Channels and Their Characteristics
`10
`1.3_ Mathematical Models for Communication Channels
`1.4 A Historical Perspective in the Development of Digital Communications 13
`1.5 Overview of the Book
`15
`1.6 Bibliographical Notes and References
`16
`
`2 Probability and Stochastic Processes
`2.1 Probability
`2.1.1 Random Variables, Probability Distributions, and Probability
`Densities | 2.1.2 Functions of RandomVariables | 2.1.3 Statistical
`Averages of Random Variables | 2.1.4 Some Useful Probability
`Distributions | 2.1.5 Upper Bounds on the Tail Probability | 2.1.6
`Sums of Random Variables and the Central Limit Theorem
`2.2 Stochastic Processes
`2.2.1 Statistical Averages | 2.2.2 Power Density Spectrum | 2.2.3
`Response of a Linear Time-Invariant System to a Random Input
`Signal / 2.2.4 Sampling Theorem for Band-Limited Stochastic
`Processes | 2.2.5 Discrete-Time Stochastic Signals and Systems | 2.2.6
`Cyelostationary Processes
`2.3. Bibliographical Notes and References
`Problems
`
`3 Source Coding
`3.1 Mathematical Models for Information Sources
`3.2 A Logarithmic Measure of Information
`3.2.1 Average Mutual Information and Entropy | 3.2.2 Information
`Measures for Continuous Random Variables
`3.3 Coding for Discrete Sources
`3.3.1 Coding for Discrete Memoryless Sources | 3.3.2 Diserete
`Stationary Sources | 3.3.3 The Lempel-Ziv Algorithm
`
`17
`17
`
`6]
`
`75
`75
`
`80
`80
`82
`
`90
`
`xl
`
`117
`
`117
`
`

`

`it
`
`Xl
`
`
`
`Contents
`
`103
`3.4 Coding for Analog Sources—Optimum Quantization
`3.4.1 Rate-Distortion Function|3.4.2 Scalar Quantization | 3.4.3
`Vector Quantization
`3.5 Coding Techniques for Analog Sources
`3.5.1 Temporal Waveform Coding | 3.5.2 Spectral Waveform
`Coding | 3.5.3 Model-Based Source Coding
`3.6 Bibliographical Notes and References
`Problems
`
`140
`141
`
`121
`
`148
`148
`
`Characterization of Communication Signals and Systems
`4.1 Representation of Band-Pass Signals and Systems
`4.1.1 Representation of Band-Pass Signals | 4.1.2 Representation of
`Linear Band-Pass Systems | 4.1.3 Response of a Band-Pass System to
`a Band-Pass Signal | 4.1.4 Representation of Band-Pass Stationary
`Stochastic Processes
`4.2 Signal Space Representations
`4.2.1 Vector Space Concepts | 4.2.2 Signal Space Concepts | 4.2.3
`Orthogonal Expansions ofSignals
`4.3 Representation of Digitally Modulated Signals
`4.3.1 Memoryless Modulation Methods | 4.3.2 Linear Modulation with
`Memory | 4.3.3 Non-linear Modulation Methods with Memory—
`CPFSK and CPM
`4.4 Spectral Characteristics of Digitally Modulated Signals
`201
`4.4.1 Power Spectra of Linearly Modulated Signals|4.4.2 Power
`Spectra of CPFSK and CPMSignals | 4.4.3 Pawer Spectra of
`Modulated Signals with Memory
`4.5. Bibliographical Notes and References
`Problems
`
`168
`
`Optimum Receivers for the Additive White Gaussian Noise
`Channel
`
`5.1 OptimumReceiver for Signals Corrupted by Additive White Gaussian
`Noise
`5.1.1 Correlation Demodulator | 5.1.2 Matched-Filter Demodulator.
`5.1.3 The Optimum Detector|5.1.4 The Maxinum-Likelihood
`Sequence Detector | 5.1.5 ASvitbol-by-Symbel MAP. Detector for
`Signals with Memory
`5.2 Performance of the Optimum Receiver for Memoryless Modulation
`5.2.1 Probability of Error for Binary Modulation | 5.2.2 Probability of
`Error for M-ary Orthogonal Signals | 5.2.3 Probability of Error for
`M-ary Biorthogonal Signals | 5.2.4 Probability of Errorfor Simplex
`Signals | 5.2.5 Probability of Error for M-ary Binary-Coded
`Signals | 5.2.6 Probability of Error for M-ary PAM | 5.2.7
`Probability of Error for M-ary PSK | 5.2.8 Differential PSK (DPSK)
`
`254
`
`Ss
`
`118
`
`118
`
`

`

`Contents
`
`xiii
`
`and Its Performance | 5.2.9 Probability of Error for QAM | 5.2.10
`Comparison of Digital Modulation Methods
`5.3 Optimum Receiver for CPM Signals
`5.3.1 Optimum Demodulation and Detection of CPM | 5.3.2
`Performance of CPM Signals | 5.3.3 Symbol-by-Symbol Detection of
`CPM Signals | 5.3.4 Suboptimum Demodulation and Detection of
`CPM Signals
`5.4 Optimum Receiver for Signals with Random Phase in AWGN Channel
`5.4.1 Optimum Receiver for Binary Signals | 5.4.2 Optimum Receiver
`for M-ary Orthogonal Signals | 5.4.3 Probability ofError for Envelope
`Detection of M-ary Orthogonal Signals | 5.4.4 Probability of Error for
`Envelope Detection of Correlated Binary Signals
`5.5. Performance Analysis for Wireline and Radio Communication Systems
`5.5.1 Regenerative Repeaters | 5.5.2 Link Budget Analysis in Radio
`Communication Systems
`5.6 Bibliographical Notes and References
`Problems
`
`283
`
`300
`
`318
`319
`
`333
`
`333
`
`6 Carrier and Symbol Synchronziation
`6.1
`Signal Parameter Estimation
`6.1.1 The Likelihood Function | 6.1.2 Carrier Recovery and Svmbal
`Synchronization in Signal Demodulation
`6.2 Carrier Phase Estimation
`6.2.1 Maximum-Likelihood Carrier Phase Estimation | 6.2.2 The
`Phase-Locked Loop | 6.2.3 Effect of Additive Noise on the Phase
`Estimate | 6.2.4 Decision-Directed Loops | 6.2.5 Non-Decision-
`Directed Loops
`6.3 Symbol Timing Estimation
`359
`6.3.1 Maximum-Likelihood Timing Estimation|6.3.2 Non-Decision-
`Directed Timing Estimation
`Joint Estimation of Carrier Phase and Symbol Timing
`6.4
`6.5 Performance Characteristics of ML Estimators
`6.6 Bibliographical Notes and References
`Problems
`
`338
`
`366
`368
`371
`372
`
`7 Channel Capacity and Coding
`7.1 Channel Models and Channel Capacity
`7.1.1 Channel Models | 7.1.2 Channel Capacity | 7.1.3 Achieving
`Channel Capacity with Orthogonal Signals | 7.1.4 Channel Reliability
`Funetions
`7.2 Random Selection of Codes
`7.2.1 Random Coding Based on M-ary Binary-Coded Signals | 7.2.2
`Random Coding Based on M-ary Multiamplitude Signals | 7.2.3
`Comparison of Ro with the Capacity of the AWGNChannel
`7.3 Communication System Design Based on the Cutoff Rate
`
`376
`
`376
`
`119
`
`119
`
`

`

`XIV
`
`7.4 Bibliographical Notes and References
`Problems
`
`Contents
`408
`409
`
`8 Block and Convolutional Channel Codes
`8.1 Linear Block Codes
`8.1.1 The Generator Matrix and the Parity Check Matrix | 8.1.2 Some
`Specific Linear Block Codes | 8.1.3 Cyclic Codes | 8.1.4 Optimum
`Soft-Decision Decoding of Linear Block Codes | 8.1.5 Hard-Decision
`Decoding of Linear Block Codes|8.1.6 C.omparison of Performance
`Between Hard-Decision and Soft-Decision Decoding | 8.1.7 Bounds on
`Minimum Distance ofLinear Block Codes | 8.1.8 Nonbinary Block
`Codes and Concatenated Block Codes|8.1.9 Interlea ving of Coded
`Data for Channels with Burst Errors | 8.1.10 Serial and Paralle|
`Concatenated Block Codes
`8.2 Convolutional Codes
`8.2.1 The Transfer Function ofa Convolutional Code / 8.2.2 Optimum
`Decoding of Convolutional Codes—The Viterbi Algorithm | 8.2.3
`Probability of Errorfor Soft-Decision Decoding | 8.2.4 Probability of
`Error for Hard-Decision Decoding | 8.2.5 Distance Properties of
`Binary Convolutional Codes|8.2.6 Punctured C.onvolutional Codes |
`8.2.7 Other Decoding Algorithmsfor Convolutional Codes | 8.2.8
`Practical Considerationsin the Application of Convolutional Codes |
`8.2.9 Nonbinary Dual-k Codes and Concatenated Codes | 8.2.10
`Parallel and Serial Concatenated Convolutional Codes
`8.3 Coded Modulation for Bandwidth-Constrained Channels—Trellis-Coded
`Modulation
`8.4 Bibliographical Notes and References
`Problems
`
`416
`416
`
`47]
`
`522
`539
`34]
`
`9 Signal Design for Band-Limited Channels
`9.1 Characterization of Band-Limited Channels
`9.2 Signal Design for Band-Limited Channels
`9.2.1 Design of Band-Limited Signals for No Intersymbol
`Interference—The Nyquist Criterion | 9.2.2 Design of Band-Limited
`Signals with Controlled 1SI—Partial-Response Signals | 9.2.3 Data
`Detection for Controlled IST | 9.2.4 Signal Design for Channels with
`Distortion
`9.3 Probability of Error in Detection of PAM
`9.3.1 Probability of Error for Detection of PAM with Zero ISI | 9.3.2
`Probability of Error for Detection ofPartial-Response Signals
`9.4 Modulation Codes for Spectrum Shaping
`9.5 Bibliographical Notes and References
`Problems
`
`548
`548
`554
`
`574
`
`578
`388
`588
`
`
`120
`
`120
`
`

`

`10 Communication Through Band-Limited Linear Filter Channels
`Optimum Receiver for Channels with ISI and AWGN
`10.1
`10.1.1 Optimum Maximum-Likelihood Receiver | 10.1.2 A Discrete-
`Time Model for a Channel with ISI | 10.1.3 The Viterbi Algorithm
`for the Discrete-Time White Noise Filter Model | 10.1.4 Performance
`of MLSE for Channels with IST
`Linear Equalization
`10.2.1 Peak Distortion Criterion | 10.2.2 Mean-Square-Error (MSE)
`Criterion | 10.2.3 Performance Characteristics of the MSE
`Equalizer | 10.2.4 Fractionally Spaced Equalizers | 10.2.5 Baseband
`and Passband Linear Equalizers
`Decision-Feedback Equalization
`10.3.1 Coefficient Optimization | 10.3.2 Performance Characteristics
`of DFE | 10.3.3 Predictive Decision-Feedback Equalizer | 10.3.4
`Equalization at the Transmitter—Tomlinson—Harashima Precoding
`Reduced Complexity ML Detectors
`Iterative Equalization and Decoding—Turbo Equalization
`Bibliographical Notes and References
`Problems
`
`10.3
`
`10.4
`10.5
`
`10.6
`
`598
`
`599
`
`616
`
`638
`
`647
`649
`651
`
`652
`
`660
`
`660
`
`677
`678
`
`682
`
`693
`
`704
`705
`
`709
`
`709
`
`Contents
`
`XV
`
`10.2
`
`11
`
`11.2
`11.3
`
`11.4
`
`Adaptive Equalization
`11.1
`Adaptive Linear Equalizer
`/1.1.1 The Zero-Forcing Algorithm | 11.1.2 The LMS Algorithm |
`11.1.3 Convergence Properties of the LMS Algorithm | 11.1.4 Excess
`MSE Due to Noisy Gradient Estimates | 11.1.5 Accelerating the
`Initial Convergence Rate in the LMS Algorithm | 11.1.6 Adaptive
`Fractionally Spaced Equalizer—The Tap Leakage Algorithm | 11.1.7
`An Adaptive Channel Estimator for ML Sequence Detection
`Adaptive Decision-Feedback Equalizer
`Adaptive Equalization of Trellis-Coded Signals
`Recursive Least-Squares Algorithms for Adaptive Equalization
`11.4.1 Recursive Least-Squares (Kalman) Algorithm | 11.4.2 Linear
`Prediction and the Lattice Filter
`Self-Recovering (Blind) Equalization
`/1.5.1 Blind Equalization Based on the Maximum-Likelihood
`Criterion | 11.5.2 Stochastic Gradient Algorithms | 11.5.3 Blind
`Equalization Algorithms Based on Second- and Higher-Order Signal
`Statistics
`Bibliographical Notes and References
`Problems
`
`11.5
`
`12
`
`Multichannel and Multicarrier Systems
`12.1 Multichannel Digital Communications in AWGN Channels
`12.1.1 Binary Signals | 12.1.2 M-ary Orthogonal Signals
`
`
`121
`
`121
`
`

`

`XVi
`
`Contents
`
`12.2) Multicarrier Communications
`| 12.2.2 An
`12.2.1 Capacity of a Nonideal Linear Filter Channel
`FFT-Based Multicarrier System|12.2.3 Minimizing Peak-to-Average
`Ratio in the Muiticarrier Sysitenis
`12.3. Bibliographical Notes and References
`Problems
`
`723
`724
`
`715
`
`13
`
`Spread Spectrum Signals for Digital Communications
`13.1 Model of Spread Spectrum Digital Communication System
`728
`729
`13.2 Direct Sequence Spread SpectrumSignals
`13.2.1 Error Rate Performance ofthe Decoder|13.2.2 Some
`Applications of DS Spread Spectrum Signals|13.2.3 Effect of Pulsed
`Interference on DS Spread Spectrum Systems|13.2.4 Excision of
`Narrowband Interference in DS Spread Spectrum Systems | 13.2.5
`Generation of PN Sequences
`13.3. Frequency-Hopped Spread Spectrum Signals
`13.3.1 Performance of FH Spread Spectrum Signals in an AWGN
`Channel|13.3.2 Performance of FH Spread Spectrum Signals in
`Partial-Band Interference|13.3.3 A CDMA System Based on FH
`Spread Spectrum Signals
`13.4 Other Types of Spread Spectrum Signals
`13.5 Synchronization of Spread Spectrum Systems
`13.6 Bibliographical Notes and References
`Problems
`
`726
`
`77
`
`784
`786
`
`792
`794
`
`14
`
`Digital Communications through Fading Multipath Channels
`14.1 Characterization of Fading Multipath Channels
`14.1.1 Channel Correlation Functions and Power Spectra | 14.1.2
`Statistical Models for Fading Channels
`14.2 The Effect of Signal Characteristics on the Choice of a Channel Model 814
`816
`14.3. Frequency-Nonselective, Slowly Fading Channel
`821
`14.4 Diversity Techniques for Fading Multipath Channels
`14.4.1 Binary Signals | 14.4.2 Multiphase Signals|14.4.3 M-ary
`Orthogonal Signals
`14.5 Digital Signaling over a Frequency-Selective, Slowly Fading Channel
`14.5.1 A Tapped-Delay-Line Channel Model | 14.5.2 The RAKE
`Demodulator|14.5.3 Performance of RAKE Demodulator | 14.5.4
`Receiver Structures for Channels with Intersymbol Interference
`14.6 Coded Waveforms for Fading Channels
`14.6.1 Probability of Error for Soft-Decision Decoding of Linear
`Binary Block Codes|14.6.2 Probability of Error for Hard-Decision
`Decoding of Linear Binary Block Codes | 14.6.3 Upper Bounds on
`the Performance of Convolutional Codes for a Rayleigh Fading
`Channel
`| 14.6.4 Use of Constant-Weight Codes and Concatenated
`Codes for a Fading Channel|14.6.5 System Design Based on the
`
`800
`
`801
`
`840
`
`
`
`122
`
`122
`
`

`

`Contents
`
`XVii
`
`Cutoff Rate|14.6.6 Performance of Coded Phase-Coherent
`Communication Systems—Bit-Interleaved Coded Modulation|14.6.7
`Trellis-Coded Modulation
`14.7 Multiple-Antenna Systems
`14.8 Bibliographical Notes and References
`Problems
`
`878
`
`885
`887
`
`896
`
`15 Multiuser Communications
`13.1
`Introduction to Multiple Access Techniques
`896
`15.2 Capacity of Multiple Access Methods
`$99
`15.3. Code-Division Multiple Access
`905
`15.3.1 CDMA Signal and Channel Models|15.3.2 The Optimum
`+
`
`Receiver|13.3.3 Suboptimum Detectors | 1 3.4 Successive
`Interference Cancellation|15.3.5 Performance Characteristics of
`Detectors
`13.4 Random Access Methods
`922
`13.4.1 ALOHA Systems and Protocols|15.4.2 Carrier Sense
`Systems and Pratacols
`15.5 Bibliographical Notes and References
`Problems
`
`Appendix A
`
`The Levinson—Durbin Algorithm
`
`Appendix B
`
`Error Probability for Multichannel Binary Signals
`
`Appendix C
`
`Error Probabilities for Adaptive Reception of /-Phase
`Signals
`C1 Mathematical Model for /-Phase Signaling Communication
`System
`C.2 Characteristic Function and Probability Density Function of
`the Phase 6
`C.3_ Error Probabilities for Slowly Rayleigh Fading Channels
`C.4 Error Probabilities for Time-Invariant and Ricean Fading
`Channels
`
`Appendix D
`
`Square-Root Factorization
`
`References and Bibliography
`
`Index
`
`939
`
`949
`
`956
`
`961
`
`963
`
`993
`
`123
`
`123
`
`

`

`APPENDIX (cid:39)(cid:40)(cid:42)(cid:36)(cid:56)(cid:39)(cid:40)(cid:49)(cid:61)(cid:44)(cid:19)(cid:20)
`APPENDIX DEGAUDENZIO01
`
`124
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`| Research Articles
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`Turbo-coded APSK modulations design for satellite broadband communications
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`Riccardo De Gaudenzi, Albert Guillén i Fabregas, Alfanso Martinez
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`Pages: 261-281 | First Published: 19 May 2006
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`APPENDIX (cid:39)(cid:40)(cid:42)(cid:36)(cid:56)(cid:39)(cid:40)(cid:49)(cid:61)(cid:44)(cid:19)(cid:21)
`APPENDIX DEGAUDENZI02
`
`131
`
`

`

`INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS AND NETWORKING
`Int. J. Satell. Commun. Network. 2006; 24:261–281
`Published online 19 May 2006 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/sat.841
`
`Turbo-coded APSK modulations design for satellite
`broadband communications
`
`Riccardo De Gaudenzi1,*,y, Albert Guille´ n i Fa` bregas2, and Alfanso Martinez3
`
`1 European Space Agency (ESA-ESTEC), Noordwijk, The Netherlands
`2 Institute for Telecommunications Research, University of South Australia, Australia
`3 Technische Universitat Eindhoven, Eindhoven, The Netherlands
`
`SUMMARY
`
`This paper investigates the design of power and spectrally efficient coded modulations based on amplitude
`phase shift keying (APSK) modulation with application to satellite broadband communications. APSK
`represents an attractive modulation format for digital transmission over nonlinear satellite channels due to
`its power and spectral efficiency combined with its inherent robustness against nonlinear distortion. For
`these reasons APSK has been very recently introduced in the new standard for satellite Digital Video
`Broadcasting named DVB-S2. Assuming an ideal rectangular transmission pulse, for which no nonlinear
`inter-symbol interference is present and perfect pre-compensation of the nonlinearity, we optimize the
`APSK constellation. In addition to the minimum distance criterion, we introduce a new optimization based
`on the mutual
`information; this new method generates an optimum constellation for each spectral
`efficiency. To achieve power efficiency jointly with l