Catalogue


Wireless communication systems : advanced techniques for signal reception /
Xiaodong Wang, H. Vincent Poor.
imprint
Upper Saddle River, NJ : Prentice Hall/PTR, c2004.
description
xv, 682 p. : ill. ; 25 cm.
ISBN
0130214353
format(s)
Book
Holdings
More Details
added author
imprint
Upper Saddle River, NJ : Prentice Hall/PTR, c2004.
isbn
0130214353
catalogue key
5298258
 
Includes bibliographical references (p. 619-662) and index.
A Look Inside
About the Author
Author Affiliation
Xiaodong Wang, Assistant Professor in the Department of Electrical Engineering, Columbia University H. Vincent Poor, Professor of Electrical Engineering at Princeton University
Excerpts
Introduction or Preface
PREFACE Wireless communications, together with its applications and underlying technologies, is among today's most active areas of technology development. The very rapid pace of improvements in both custom and programmable integrated circuits for signal processing applications has led to the justifiable view of advanced signal processing as a key enabler of the aggressively escalating capacity demands of emerging wireless systems. Consequently, there has been a tremendous and very widespread effort on the part of the research community to develop novel signal processing techniques that can fulfill this promise. The published literature in this area has grown explosively in recent years, and it has become quite diffcult to synthesize the many developments described in this literature. The purpose of this book is to present, in one place and in a unified framework, a number of key recent contributions in this field. Even though these contributions come primarily from the research community, the focus of this presentation is on the development, analysis, and understanding of explicit algorithms for performing advanced processing tasks arising in receiver design for emerging wireless systems. Although this book is largely self-contained, it is written principally for designers, researchers, and graduate students with some prior exposure to wireless communication systems. Knowledge of the field at the level of Theodore Rappaport's book, Wireless Communications: Principles and Practice, for example, would be quite useful to the reader of this book, as would some exposure to digital communications at the level of John Proakis's book, Digital Communications. Acknowledgments The authors would like to thank the Army Research Laboratory, the National Science Foundation, the New Jersey Commission on Science and Technology, and the Offce of Naval Research for their support of much of the research described in this book.
Introduction or Preface
Preface Preface: (0132291959//6A3RM-3) View History Preface Green belts working in a Six Sigma organization need to be familiar with statistics because statistical tools are an essential part of each phase of the Six Sigma DMAIC model. This book focuses on those statistical tools that are most important for Six Sigma green belts. The objective of this book is to familiarize readers with these tools, so that they will be able to use either the Minitab or JMP software to analyze their data. Among the important features of this book are the following: Provides a simple nonmathematical presentation of topics. Every concept is explained in plain English with a minimum of mathematical symbols. Most of the equations are separated into optional boxes that complement the main material. Covers the statistical topics that are most important for Six Sigma green belts. After beginning with the use of tables and charts and descriptive statistics, the book includes coverage of those statistical topics that are most important for Six Sigma green belts; i.e., statistical process control charts, design of experiments, and regression. This book minimizes emphasis on probability, probability distributions, and sampling distributions. Covers statistical topics by focusing on the interpretation of output generated by the Minitab and JMP software. Includes chapter-ending appendices that provide step-by-step instructions (with screenshots of dialog boxes) for using Minitab Version 14 and JMP Version 6 for the statistical topics covered in the chapter. Provides step-by-step instructions using worked-out examples for each statistical method covered. Includes service industry applications. This book focuses on applications of Six Sigma in service industries. These service applications enable those who work in service industries to understand the role of Six Sigma in their industries, something that is difficult for them to see when examples from manufacturing industries are used. Contacting the Author I have gone to great lengths to make this book both pedagogically sound and error-free. If you have any suggestions or require clarification about any of the material, or if you find any errors, contact me at DAVID_LEVINE@BARUCH.CUNY.EDU David M. Levine ¿ Copyright Pearson Education. All rights reserved.
First Chapter
Preface Preface: (0132291959//6A3RM-3) View History Preface Green belts working in a Six Sigma organization need to be familiar with statistics because statistical tools are an essential part of each phase of the Six Sigma DMAIC model. This book focuses on those statistical tools that are most important for Six Sigma green belts. The objective of this book is to familiarize readers with these tools, so that they will be able to use either the Minitab or JMP software to analyze their data. Among the important features of this book are the following: Provides a simple nonmathematical presentation of topics. Every concept is explained in plain English with a minimum of mathematical symbols. Most of the equations are separated into optional boxes that complement the main material. Covers the statistical topics that are most important for Six Sigma green belts. After beginning with the use of tables and charts and descriptive statistics, the book includes coverage of those statistical topics that are most important for Six Sigma green belts; i.e., statistical process control charts, design of experiments, and regression. This book minimizes emphasis on probability, probability distributions, and sampling distributions. Covers statistical topics by focusing on the interpretation of output generated by the Minitab and JMP software. Includes chapter-ending appendices that provide step-by-step instructions (with screenshots of dialog boxes) for using Minitab Version 14 and JMP Version 6 for the statistical topics covered in the chapter. Provides step-by-step instructions using worked-out examples for each statistical method covered. Includes service industry applications. This book focuses on applications of Six Sigma in service industries. These service applications enable those who work in service industries to understand the role of Six Sigma in their industries, something that is difficult for them to see when examples from manufacturing industries are used. Contacting the Author I have gone to great lengths to make this book both pedagogically sound and error-free. If you have any suggestions or require clarification about any of the material, or if you find any errors, contact me at DAVID_LEVINE@BARUCH.CUNY.EDU David M. Levine ¿ Copyright Pearson Education. All rights reserved.
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Summaries
Back Cover Copy
A unified framework for using today's most advanced signal processing techniques Driven by the rapidly escalating capacity demands of emerging wireless systems, researchers havedeveloped a wide array of novel signal processing techniques for use in such systems. Now, twoleading researchers synthesize the field's vast new literature, giving working engineers practicalguidance for designing advanced wireless receivers. Drs. Xiaodong Wang and H. Vincent Poor offer a complete framework for developing, analyzing, andunderstanding the explicit algorithms needed for advanced processing in emerging wireless systems.They address a full range of physical-layer issues, including multipath, dispersion, interference,dynamism, and multiple-antenna systems. In many cases, the authors themselves developed the methodsthey present. Coverage includes: An overview of contemporary wireless signaling environments and basic receiver signal processing techniques Blind, group-blind, space-time, and turbo multiuser detection Robust multiuser detection in non-Gaussian channels Narrowband interference suppression: linear and non-linear predictive techniques, performance comparisons, and more Monte Carlo Bayesian signal processing Signal processing for fast fading channels Advanced signal processing for coded OFDM systems
Main Description
Many of the methods detailed in the book were developed by the authors. Presentation of a number of key recent contributions to advanced signal processing for wireless communications in one place and in a unified framework. Focuses on explicit algorithms for performing advanced processing tasks arising in receiver design for emerging wireless systems.
Main Description
Wireless Communication Systems: Advanced Techniques for Signal Receptionoffers a unified frameworkfor understanding today's newest techniques for signal processing in communication systems - andusing them to design receivers for emerging wireless systems. Two leading researchers cover a fullrange of physical-layer issues, including multipath, dispersion, interference, dynamism, andmultiple-antenna systems. Topics include blind, group-blind, space-time, and turbo multiuserdetection; narrowband interference suppression; Monte Carlo Bayesian signal processing; fast fadingchannels; advanced signal processing in coded OFDM systems, and more.
Long Description
Appropriate for all courses in wireless receiver design, and for advanced courses in signal processing. As the performance and power requirements for wireless devices become increasingly challenging, engineers have recognized that advanced signal processing techniques are more crucial than ever. This book presents a unified framework for understanding the state-of-the-art in signal processing for wireless communications. Dr. Xiadong Wang and Dr. H. Vincent Poor focus on the development, analysis, and use of explicit algorithms for performing advanced processing tasks that arise in receiver design for emerging wireless systems, and provide a comprehensive set of algorithms for addressing physical issues, including multi-path, dispersion, interference, dynamism, and multiple-antenna systems. Many of the methods detailed here were developed by the co-authors themselves, notably in the areas of turbo processing, multiple-antenna systems, and low-complexity adaptive algorithms.
Long Description
Appropriate for all courses in wireless receiver design, and for advanced courses in signal processing.As the performance and power requirements for wireless devices become increasingly challenging, engineers have recognized that advanced signal processing techniques are more crucial than ever. This book presents a unified framework for understanding the state-of-the-art in signal processing for wireless communications.Dr. Xiadong Wang and Dr. H. Vincent Poor focus on the development, analysis, and use of explicit algorithms for performing advanced processing tasks that arise in receiver design for emerging wireless systems, and provide a comprehensive set of algorithms for addressing physical issues, including multi-path, dispersion, interference, dynamism, and multiple-antenna systems. Many of the methods detailed here were developed by the co-authors themselves, notably in the areas of turbo processing, multiple-antenna systems, and low-complexity adaptive algorithms.
Back Cover Copy
A unified framework for using today's most advanced signal processing techniquesDriven by the rapidly escalating capacity demands of emerging wireless systems, researchers havedeveloped a wide array of novel signal processing techniques for use in such systems. Now, twoleading researchers synthesize the field's vast new literature, giving working engineers practicalguidance for designing advanced wireless receivers.Drs. Xiaodong Wang and H. Vincent Poor offer a complete framework for developing, analyzing, andunderstanding the explicit algorithms needed for advanced processing in emerging wireless systems.They address a full range of physical-layer issues, including multipath, dispersion, interference,dynamism, and multiple-antenna systems. In many cases, the authors themselves developed the methodsthey present. Coverage includes: An overview of contemporary wireless signaling environments and basic receiver signal processing techniques Blind, group-blind, space-time, and turbo multiuser detection Robust multiuser detection in non-Gaussian channels Narrowband interference suppression: linear and non-linear predictive techniques, performance comparisons, and more Monte Carlo Bayesian signal processing Signal processing for fast fading channels Advanced signal processing for coded OFDM systems
Table of Contents
Prefacep. xv
Introductionp. 1
Motivationp. 1
Wireless Signaling Environmentp. 3
Single-User Modulation Techniquesp. 3
Multiple-Access Techniquesp. 5
Wireless Channelp. 7
Basic Receiver Signal Processing for Wirelessp. 13
Matched Filter/RAKE Receiverp. 13
Equalizationp. 17
Multiuser Detectionp. 19
Outline of the Bookp. 21
Blind Multiuser Detectionp. 27
Introductionp. 27
Linear Receivers for Synchronous CDMAp. 28
Synchronous CDMA Signal Modelp. 28
Linear Decorrelating Detectorp. 30
Linear MMSE Detectorp. 31
Blind Multiuser Detection: Direct Methodsp. 32
LMS Algorithmp. 34
RLS Algorithmp. 35
QR-RLS Algorithmp. 37
Blind Multiuser Detection: Subspace Methodsp. 41
Linear Decorrelating Detectorp. 41
Linear MMSE Detectorp. 43
Asymptotics of Detector Estimatesp. 45
Asymptotic Multiuser Efficiency under Mismatchp. 46
Performance of Blind Multiuser Detectorsp. 49
Performance Measuresp. 49
Asymptotic Output SINRp. 51
Subspace Tracking Algorithmsp. 59
PASTd Algorithmp. 62
QR-Jacobi Methodsp. 66
NAHJ Subspace Trackingp. 68
Blind Multiuser Detection in Multipath Channelsp. 71
Multipath Signal Modelp. 71
Linear Multiuser Detectorsp. 73
Blind Channel Estimationp. 77
Adaptive Receiver Structuresp. 82
Blind Multiuser Detection in Correlated Noisep. 86
Appendixp. 93
Derivations for Section 2.3.3p. 93
Proofs for Section 2.4.4p. 95
Proofs for Section 2.5.2p. 96
Group-Blind Multiuser Detectionp. 109
Introductionp. 109
Linear Group-Blind Multiuser Detection for Synchronous CDMAp. 110
Performance of Group-Blind Multiuser Detectorsp. 119
Form II Group-Blind Hybrid Detectorp. 119
Form I Group-Blind Detectorsp. 125
Nonlinear Group-Blind Multiuser Detection for Synchronous CDMAp. 129
Slowest-Descent Searchp. 133
Nonlinear Group-Blind Multiuser Detectionp. 135
Group-Blind Multiuser Detection in Multipath Channelsp. 140
Linear Group-Blind Detectorsp. 143
Adaptive Group-Blind Linear Multiuser Detectionp. 151
Linear Group-Blind Detection in Correlated Noisep. 155
Nonlinear Group-Blind Detectionp. 158
Appendixp. 161
Proofs for Section 3.3.1p. 161
Proofs for Section 3.3.2p. 166
Robust Multiuser Detection in Non-Gaussian Channelsp. 173
Introductionp. 173
Multiuser Detection via Robust Regressionp. 175
System Modelp. 175
Least-Squares Regression and Linear Decorrelatorp. 176
Robust Multiuser Detection via M-Regressionp. 177
Asymptotic Performance of Robust Multiuser Detectionp. 182
Influence Functionp. 182
Asymptotic Probability of Errorp. 183
Implementation of Robust Multiuser Detectorsp. 187
Robust Blind Multiuser Detectionp. 197
Robust Multiuser Detection Based on Local Likelihood Searchp. 201
Exhaustive-Search and Decorrelative Detectionp. 201
Local-Search Detectionp. 204
Robust Group-Blind Multiuser Detectionp. 206
Extension to Multipath Channelsp. 211
Robust Blind Multiuser Detection in Multipath Channelsp. 212
Robust Group-Blind Multiuser Detection in Multipath Channelsp. 213
Robust Multiuser Detection in Stable Noisep. 215
Symmetric Stable Distributionp. 216
Performance of Robust Multiuser Detectors in Stable Noisep. 219
Appendixp. 222
Proof of Proposition 4.1 in Section 4.4p. 222
Proof of Proposition 4.2 in Section 4.5p. 223
Space-Time Multiuser Detectionp. 225
Introductionp. 225
Adaptive Array Processing in TDMA Systemsp. 226
Signal Modelp. 226
Linear MMSE Combiningp. 228
Subspace-Based Training Algorithmp. 230
Extension to Dispersive Channelsp. 237
Optimal Space-Time Multiuser Detectionp. 239
Signal Modelp. 241
Sufficient Statisticp. 242
Maximum-Likelihood Multiuser Sequence Detectorp. 245
Linear Space-Time Multiuser Detectionp. 247
Linear Multiuser Detection via Iterative Interference Cancellationp. 247
Single-User Linear Space-Time Detectionp. 251
Combined Single-User/Multiuser Linear Detectionp. 254
Adaptive Space-Time Multiuser Detection in Synchronous CDMAp. 265
One Transmit Antenna, Two Receive Antennasp. 268
Two Transmit Antennas, One Receive Antennap. 273
Two Transmit and Two Receive Antennasp. 277
Blind Adaptive Implementationsp. 281
Adaptive Space-Time Multiuser Detection in Multipath CDMAp. 287
Signal Modelp. 287
Blind MMSE Space-Time Multiuser Detectionp. 294
Blind Adaptive Channel Estimationp. 295
Turbo Multiuser Detectionp. 303
Introduction to Turbo Processingp. 303
MAP Decoding Algorithm for Convolutional Codesp. 308
Turbo Multiuser Detection for Synchronous CDMAp. 313
Turbo Multiuser Receiverp. 313
Optimal SISO Multiuser Detectorp. 317
Low-Complexity SISO Multiuser Detectorp. 319
Turbo Multiuser Detection with Unknown Interferersp. 328
Signal Modelp. 328
Group-Blind SISO Multiuser Detectorp. 329
Sliding Window Group-Blind Detector for Asynchronous CDMAp. 335
Turbo Multiuser Detection in CDMA with Multipath Fadingp. 339
Signal Model and Sufficient Statisticsp. 339
SISO Multiuser Detector in Multipath Fading Channelsp. 342
Turbo Multiuser Detection in CDMA with Turbo Codingp. 346
Turbo Code and Soft Decoding Algorithmp. 346
Turbo Multiuser Receiver in Turbo-Coded CDMA with Multipath Fadingp. 351
Turbo Multiuser Detection in Space-Time Block-Coded Systemsp. 356
Multiuser STBC Systemp. 357
Turbo Multiuser Receiver for STBC Systemp. 361
Projection-Based Turbo Multiuser Detectionp. 367
Turbo Multiuser Detection in Space-Time Trellis-Coded Systemsp. 370
Multiuser STTC Systemp. 370
Turbo Multiuser Receiver for STTC Systemp. 373
Appendixp. 380
Proofs for Section 6.3.3p. 380
Derivation of the LLR for the RAKE Receiver in Section 6.6.2p. 381
Narrowband Interference Suppressionp. 385
Introductionp. 385
Linear Predictive Techniquesp. 390
Signal Modelsp. 390
Linear Predictive Methodsp. 392
Nonlinear Predictive Techniquesp. 396
ACM Filterp. 397
Adaptive Nonlinear Predictorp. 400
Nonlinear Interpolating Filtersp. 403
HMM-Based Methodsp. 407
Code-Aided Techniquesp. 407
NBI Suppression via the Linear MMSE Detectorp. 408
Tonal Interferencep. 410
Autoregressive Interferencep. 414
Digital Interferencep. 416
Performance Comparisons of NBI Suppression Techniquesp. 419
Matched Filterp. 420
Linear Predictor and Interpolatorp. 420
Nonlinear Predictor and Interpolatorp. 421
Numerical Examplesp. 423
Near-Far Resistance to Both NBI and MAI by Linear MMSE Detectorp. 424
Near-Far Resistance to NBIp. 424
Near-Far Resistance to Both NBI and MAIp. 426
Adaptive Linear MMSE NBI Suppressionp. 429
Maximum-Likelihood Code-Aided Methodp. 431
Appendix: Convergence of the RLS Linear MMSE Detectorp. 435
Linear MMSE Detector and RLS Blind Adaptation Rulep. 435
Convergence of the Mean Weight Vectorp. 437
Weight Error Correlation Matrixp. 440
Convergence of MSEp. 443
Steady-State SINRp. 444
Comparison with Training-Based RLS Algorithmp. 445
Monte Carlo Bayesian Signal Processingp. 447
Introductionp. 447
Bayesian Signal Processingp. 448
Bayesian Frameworkp. 448
Batch Processing versus Adaptive Processingp. 449
Monte Carlo Methodsp. 451
Markov Chain Monte Carlo Signal Processingp. 451
Metropolis-Hastings Algorithmp. 452
Gibbs Samplerp. 453
Bayesian Multiuser Detection via MCMCp. 455
System Descriptionp. 455
Bayesian Multiuser Detection in Gaussian Noisep. 458
Bayesian Multiuser Detection in Impulsive Noisep. 464
Bayesian Multiuser Detection in Coded Systemsp. 469
Sequential Monte Carlo Signal Processingp. 477
Sequential Importance Samplingp. 477
SMC for Dynamical Systemsp. 482
Resampling Proceduresp. 485
Mixture Kalman Filterp. 487
Blind Adaptive Equalization of MIMO Channels via SMCp. 488
System Descriptionp. 489
SMC Blind Adaptive Equalizer for MIMO Channelsp. 490
Appendixp. 495
Derivations for Section 8.4.2p. 495
Derivations for Section 8.4.3p. 496
Proof of Proposition 8.1 in Section 8.5.2p. 498
Proof of Proposition 8.2 in Section 8.5.3p. 499
Signal Processing for Fading Channelsp. 501
Introductionp. 501
Statistical Modeling of Multipath Fading Channelsp. 504
Frequency-Nonselective Fading Channelsp. 505
Frequency-Selective Fading Channelsp. 506
Coherent Detection in Fading Channels Based on the EM Algorithmp. 507
Expectation-Maximization Algorithmp. 507
EM-Based Receiver in Flat-Fading Channelsp. 508
Linear Multiuser Detection in Flat-Fading Synchronous CDMA Channelsp. 511
Sequential EM Algorithmp. 512
Decision-Feedback Differential Detection in Fading Channelsp. 514
Decision-Feedback Differential Detection in Flat-Fading Channelsp. 514
Decision-Feedback Space-Time Differential Decodingp. 516
Adaptive SMC Receivers for Flat-Fading Channelsp. 523
System Descriptionp. 527
Adaptive Receiver in Fading Gaussian Noise Channels: Uncoded Casep. 531
Delayed Estimationp. 534
Adaptive Receiver in Fading Gaussian Noise Channels: Coded Casep. 541
Adaptive Receivers in Fading Impulsive Noise Channelsp. 544
Appendixp. 549
Proof of Proposition 9.1 in Section 9.5.2p. 549
Advanced Signal Processing for Coded OFDM Systemsp. 551
Introductionp. 551
OFDM Communication Systemp. 552
Blind MCMC Receiver for Coded OFDM with Frequency-Selective Fading and Frequency Offsetp. 555
System Descriptionp. 556
Bayesian MCMC Demodulatorp. 559
Pilot-Symbol-Aided Turbo Receiver for Space-Time Block-Coded OFDM Systemsp. 569
System Descriptionsp. 569
ML Receiver Based on the EM Algorithmp. 575
Pilot-Symbol-Aided Turbo Receiverp. 581
LDPC-Based Space-Time Coded OFDM Systemsp. 588
Capacity Considerations for STC-OFDM Systemsp. 589
Low-Density Parity-Check Codesp. 596
LDPC-Based STC-OFDM Systemp. 599
Turbo Receiverp. 601
Appendixp. 612
Derivations for Section 10.3p. 612
Acronymsp. 615
Bibliographyp. 619
Indexp. 663
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