Nonlinear Distortion in Wireless Systems E-Book

Khaled M. Gharaibeh

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Wiley - IEEE
• Sprache: Englisch

Seitenzahl: 477

Veröffentlichungsjahr: 2011

Beschreibung

This book covers the principles of modeling and simulation of nonlinear distortion in wireless communication systems with MATLAB simulations and techniques

In this book, the author describes the principles of modeling and simulation of nonlinear distortion in single and multichannel wireless communication systems using both deterministic and stochastic signals. Models and simulation methods of nonlinear amplifiers explain in detail how to analyze and evaluate the performance of data communication links under nonlinear amplification. The book addresses the analysis of nonlinear systems with stochastic inputs and establishes the performance metrics of communication systems with regard to nonlinearity. In addition, the author also discusses the problem of how to embed models of distortion in system-level simulators such as MATLAB and MATLAB Simulink and provides practical techniques that professionals can use on their own projects. Finally, the book explores simulation and programming issues and provides a comprehensive reference of simulation tools for nonlinearity in wireless communication systems.

Key Features:

• Covers the theory, models and simulation tools needed for understanding nonlinearity and nonlinear distortion in wireless systems
• Presents simulation and modeling techniques for nonlinear distortion in wireless channels using MATLAB
• Uses random process theory to develop simulation tools for predicting nonlinear system performance with real-world wireless communication signals
• Focuses on simulation examples of real-world communication systems under nonlinearity
• Includes an accompanying website containing MATLAB code

This book will be an invaluable reference for researchers, RF engineers, and communication system engineers working in the field. Graduate students and professors undertaking related courses will also find the book of interest.

Leseprobe

Table of Contents

Title Page

Copyright

Dedication

Preface

List of Abbreviations

List of Figures

List of Tables

Acknowledgements

Chapter 1: Introduction

1.1 Nonlinearity in Wireless Communication Systems

1.2 Nonlinear Distortion in Wireless Systems

1.3 Modeling and Simulation of Nonlinear Systems

1.4 Organization of the Book

1.5 Summary

Chapter 2: Wireless Communication Systems, Standards and Signal Models

2.1 Wireless System Architecture

2.2 Digital Signal Processing in Wireless Systems

2.3 Mobile System Standards

2.4 Wireless Network Standards

2.5 Nonlinear Distortion in Different Wireless Standards

2.6 Summary

Chapter 3: Modeling of Nonlinear Systems

3.1 Analytical Nonlinear Models

3.2 Empirical Nonlinear Models

3.3 Parameter Extraction of Nonlinear Models from Measured Data

3.4 Summary

Chapter 4: Nonlinear Transformation of Deterministic Signals

4.1 Complex Baseband Analysis and Simulations

4.2 Complex Baseband Analysis of Memoryless Nonlinear Systems

4.3 Complex Baseband Analysis of Nonlinear Systems with Memory

4.4 Complex Envelope Analysis with Multiple Bandpass Signals

4.5 Examples–Response of Power-Series Model to Multiple Signals

4.6 Summary

Chapter 5: Nonlinear Transformation of Random Signals

5.1 Preliminaries

5.2 Linear Systems with Stochastic Inputs

5.3 Response of a Nonlinear System to a Random Input Signal

5.4 Response of Nonlinear Systems to Gaussian Inputs

5.5 Response of Nonlinear Systems to Multiple Random Signals

5.6 Response of Nonlinear Systems to a Random Signal and a Sinusoid

5.7 Summary

Chapter 6: Nonlinear Distortion

6.1 Identification of Nonlinear Distortion in Digital Wireless Systems

6.2 Orthogonalization of the Behavioral Model

6.3 Autocorrelation Function and Spectral Analysis of the Orthogonalized Model

6.4 Relationship Between System Performance and Uncorrelated Distortion

6.5 Examples

6.6 Measurement of Uncorrelated Distortion

6.7 Summary

Chapter 7: Nonlinear System Figures of Merit

7.1 Analogue System Nonlinear Figures of Merit

7.2 Adjacent-Channel Power Ratio (ACPR)

7.3 Signal-to-Noise Ratio (SNR)

7.4 CDMA Waveform Quality Factor (ρ)

7.5 Error Vector Magnitude (EVM)

7.6 Co-Channel Power Ratio (CCPR)

7.7 Noise-to-Power Ratio (NPR)

7.8 Noise Figure in Nonlinear Systems

7.9 Summary

Chapter 8: Communication System Models and Simulation in MATLAB®

8.1 Simulation of Communication Systems

8.2 Choosing the Sampling Rate in MATLAB® Simulations

8.3 Random Signal Generation in MATLAB®

8.4 Pulse-Shaping Filters

8.5 Error Detection and Correction

8.6 Digital Modulation in MATLAB®

8.7 Channel Models in MATLAB®

8.8 Simulation of System Performance in MATLAB®

8.9 Generation of Communications Signals in MATLAB®

8.10 Example

8.11 Random Signal Generation in Simulink®

8.12 Digital Modulation in Simulink®

8.13 Simulation of System Performance in Simulink®

8.14 Summary

Chapter 9: Simulation of Nonlinear Systems in MATLAB®

9.1 Generation of Nonlinearity in MATLAB®

9.2 Fitting a Nonlinear Model to Measured Data

9.3 Autocorrelation and Spectrum Estimation

9.4 Spectrum of the Output of a Memoryless Nonlinearity

9.5 Spectrum of the Output of a Nonlinearity with Memory

9.6 Spectrum of Orthogonalized Nonlinear Model

9.7 Estimation of System Metrics from Simulated Spectra

9.8 Simulation of Probability of Error

9.9 Simulation of Noise-to-Power Ratio

9.10 Simulation of Nonlinear Noise Figure

9.11 Summary

Chapter 10: Simulation of Nonlinear Systems in Simulink®

10.1 RF Impairments in Simulink®

10.2 Nonlinear Amplifier Mathematical Models in Simulink®

10.3 Nonlinear Amplifier Physical Models in Simulink®

10.4 Measurements of Distortion and System Metrics

10.5 Example: Performance of Digital Modulation with Nonlinearity

10.6 Simulation of Noise-to-Power Ratio

10.7 Simulation of Noise Figure in Nonlinear Systems

10.8 Summary

Appendix A: Basics of Signal and System Analysis

A.1 Signals

A.2 Systems

Appendix B: Random Signal Analysis

B.1 Random Variables

B.2 Two Random Variables

B.3 Multiple Random Variables

B.4 Complex Random Variables

B.5 Gaussian Random Variables

B.6 Random Processes

B.7 The Power Spectrum

Appendix C: Introduction to MATLAB®

C.1 MATLAB® Scripts

C.2 MATLAB® Structures

C.3 MATLAB® Graphics

C.4 Random Number Generators

C.5 Moments and Correlation Functions of Random Sequences

C.6 Fourier Transformation

C.7 MATLAB® Toolboxes

C.8 Simulink®

References

Index

This edition first published 2012

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Chapters 3, 4 and 5 contain some material taken from the following sources: 1. Khaled M. Gharaibeh, Kevin Gard and M. B. Steer, “In-band Distortion of Multisines,” IEEE Transaction on Microwave Theory and Techniques, Vol. 54, pp. 3227–3236, Aug. 2006. 2. Khaled. M. Gharaibeh and M. B. Steer, “Modeling distortion in multichannel communication systems,” IEEE Trans. Microwave Theory and Tech., Vol. 53, No. 5, pp. 1682–1692, May 2005. 3. Khaled Gharaibeh and M. B. Steer, “Characterization of Cross Modulation in Multi-channel Power Amplifiers Using a Statistically Based Behavioral Modeling Technique,” IEEE Transaction on Microwave Theory and Techniques, Dec. 2003. Reproduced by permission 2003, 2005, 2006 of © IEEE.

Chapter 6 contains some material taken from the following source: Khaled M. Gharaibeh, Kevin Gard and M. B. Steer, “Estimation of Co-Channel Nonlinear Distortion and SNDR in Wireless System,” IET Microwave Antenna and Propagation, 2007, Volume 1, Issue 5, pp. 1078–1085. Reproduced by permission of © 2007 IET.

Chapter 7 contains some material taken from the following sources: 1. Reprinted from AEU—International Journal of Electronics and Communications, 64, Khaled M. Gharaibeh, “On the relationship between the noise-to-power ratio (NPR) and the effective in-band distortion of WCDMA signals,” 273–279, © 2010, with permission from Elsevier. 2. Khaled M. Gharaibeh, 2009. Simulation of Noise Figure of Nonlinear Amplifiers Using the Orthogonalization of the Nonlinear Model. International J. RF Microwave Comput-Eng. 19, 502–511. Reproduced with permission from John Wiley & Sons, Ltd. 3. Khaled M. Gharaibeh, K. Gard and M. B. Steer, 2004. Accurate Estimation of Digital Communication System Metrics—SNR, EVM and Rho in a Nonlinear Amplifier Environment. In Proc. of the 42 Automatic RF Techniques Group (ARFTG) Orlando, FL, pp. 41–44.

MATLAB® and Simulink® are trademarks of The MathWorks, Inc. and are used with permission. The MathWorks does not warrant the accuracy of the text or exercises in this book. This book's use or discussion of MATLAB® and Simulink® software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB® and Simulink® software.

Library of Congress Cataloging-in-Publication Data

Gharaibeh, Khaled M.

Nonlinear distortion in wireless systems : modeling and simulation with MATLAB® / Khaled M. Gharaibeh.

p. cm.

Includes bibliographical references and index.

ISBN 978-0-470-66104-8 (hardback)

1. Signal processing–Computer simulation. 2. Electric distortion–Computer simulation. 3. Nonlinear systems–Computer simulation. 4. Wireless communication systems–Computer simulation. 5. MATLAB®. I. Title.

TK5102.9.G48 2012

621.382′2028553–dc23

2011029722

A catalogue record for this book is available from the British Library.

ISBN: 9780470661048 (H/B)

ISBN: 9781119961727 (ePDF)

ISBN: 9781119961734 (oBook)

ISBN: 9781119964117 (ePub)

ISBN: 9781119964124 (Mobi)

To my wife Rania and my sons Mohammed, Ibrahim and Abdullah…

Preface

Modeling and simulation of nonlinear systems provide communication system designers with a tool to predict and verify overall system performance under nonlinearity and complex communication signals. Traditionally, RF system designers use deterministic signals (discrete tones), which can be implemented in circuit simulators, to predict the performance of their nonlinear circuits/systems. However, RF system designers are usually faced with the problem of predicting system performance when the input to the system is real-world communication signals which have a random nature. In this case, system distortion cannot be quantified and simulated using traditional approaches which do not take into account the random nature of real-world communication signals. Many books which discuss modeling and simulation of nonlinear system exist. However, and up to the knowledge of the author, very few of them have targeted modeling and simulation of nonlinear distortion from a stochastic point of view.

This book describes the principles of modeling and simulation of nonlinear distortion in single and multichannel wireless communication systems using both deterministic and stochastic signals. Models and simulation methods of nonlinear amplifiers explain in detail how to analyze and evaluate the performance of data communication links and how to establish the performance metrics under nonlinear transformations. The book relies extensively on using random process theory to develop simulation tools for predicting system performance. The analysis presented in the book provides a linkage between deterministic and stochastic views of nonlinear distortion which enables system and circuit designers to understand the nonlinear phenomena and hence, to be able to design wireless communication systems efficiently.

This book also addresses the problem of how to embed models of nonlinear distortion in system-level simulators such as MATLAB® and Simulink® where practical techniques that professionals can use immediately on their projects are presented. It provides MATLAB® simulation modules and a comprehensive reference of models needed for the simulation of nonlinear distortion in wireless communication systems. The book explores simulation and programming issues and provides a comprehensive reference of simulation tools for nonlinearity in wireless communication systems. Together, these provide a powerful resource for students, professors and engineers who are working on the design and verification of nonlinear systems such as High-Power Amplifiers (HPA), Low-Noise Amplifiers (LNA), mixers, etc. in the context of a wireless communication system design.

The book is divided into three major parts totaling ten chapters. The first part consists of three chapters and provides the basics needed to understand the nonlinear phenomena and discusses the basics of modeling nonlinearity in wireless systems. Chapter 1 is an introduction to wireless communications systems and nonlinearity and it serves as an introduction to the problem of modeling nonlinear distortion. Chapter 2 is an introduction to wireless system standards and signal models; and Chapter 3 presents various models of nonlinearity and their parameter extraction. This includes analytical models such as Volterra based models as well empirical model such as block models.

The second part consists of three chapters and discusses major techniques used for characterizing nonlinear distortion in wireless communication systems. Two major approaches are presented; the first is based on modeling deterministic signal distortion and the second utilizes random signals to characterize nonlinear distortion in real-world communication systems. Chapter 4 provides the reader with the deterministic view of nonlinear distortion where it discusses the analysis of nonlinear distortion using single and multiple tones. Closed form expressions that relate signal distortion to nonlinear system characteristics are presented for various nonlinear models such as power series, Volterra model and block models. Chapter 5 discusses analysis of the response of nonlinear system to a random input signals which represent real-world communication signals. This chapter demonstrates the probabilistic view of nonlinear distortion and provides the basic mathematical tools needed to analyze nonlinear distortion which include autocorrelation function analysis and nonlinear spectral analysis. It also discusses the analysis of nonlinear distortion in multichannel systems using random signals. Chapter 6 presents the concept of the orthogonalization of the behavioral model which is used to identify in-band distortion components responsible for the degradation of wireless system performance. Finally, Chapter 2001 uses the concepts developed in the previous chapters and presents the derivation of communication system figures of merit in terms of nonlinearity. These include Adjacent Channel Power Ratio (ACPR), Signal-to-Noise and Distortion Ratio (SNDR), Noise-to-Power Ratio (NPR), Error Vector Magnitude (EVM), Noise Figure (NF) and Bit Error Rate (BER).

The last part provides the reader with techniques for implementing various models of nonlinearity and nonlinear distortion in MATLAB® and Simulink®. Chapter 8 provides an introduction to the simulation of communication systems in MATLAB® and in Simulink®, where the basics of simulations of modern wireless communication systems are presented. Chapter 9 explains how to use MATLAB® to simulate various types of nonlinearity and how to analyze, predict and evaluate the performance of data communication systems under nonlinearity. Finally, Chapter 10 explains how to use Simulink® to analyze, predict and evaluate the performance of wireless communication systems related to nonlinear distortion and provides a comprehensive reference of models for simulation of nonlinear distortion.

To complement the material of the book chapters, three appendices are included which serve as supporting material. Appendix A provides the basics of signal and system analysis which includes time and frequency representation of signals and linear system analysis. Appendix B provides an introduction to random variables and random processes on which, the bulk of the material of the book is based, and Appendix C provides an introduction to MATLAB® and MATLAB® simulations.

For more information, please visit the companion website, www.wiley.com/go/gharaibeh_modeling.

Khaled M. Gharaibeh, Irbid, Jordan

List of Abbreviations

AMPSAdvanced Mobile Phone SystemADSAdvanced Design SystemADCAnalog-to-Digital ConverterAM–AMAmplitude Modulation–Amplitude ModulationAM–PMAmplitude Modulation–Phase ModulationACPRAdjacent-Channel Power RatioASKAmplitude Shift KeyingAWGNAdditive White Gaussian NoiseBERBit Error RateBPFBand Pass filterCCPRCo-Channel Power RatioCDMACode Division Multiple AccessCWContinuous WaveDACDigital-to-Analog ConversionDPCHDedicated Physical ChannelDCSDigital Cellular SystemDUTDevice Under TestDSPDigital Signal ProcessingDS-SSDirect Sequence Spread SpectrumDECTDigital European Cordless TelephoneDLDown-LinkdBDecibelEVMError Vector MagnitudeEDGEEnhanced Data rates for GSM EvolutionETSIEuropean Telecommunications Standards InstituteFSKFrequency Shift KeyingFDMAFrequency Division Multiple AccessGSMGlobal System for MobileGMSKGaussian Minimum Shift KeyingGHzGigahertzGPSGeneralized Power SeriesHBHarmonic BalanceISIInter Symbol InterferenceICIInter Channel InterferenceIFIntermediate FrequencyIMDIntermodulation DistortionIMRIntermodulation RatioIIP3Input Third-Order Intercept PointLANLocal Area NetworkLNALow Noise AmplifierLTELong Term EvolutionLPFLow Pass FilterLOSLine-of-SightMHzMegaHertzMISOMultiple Input Single OutputNPRNoise-to-Power RatioNFNoise FigureNNFNonlinear Noise FigureNBGNNarrow band Gaussian NoiseNIINational Information InfrastructureNLOSNon-Line-Of-SightOIP3Output Third-Order Intercept PointOFDMOrthogonal Frequency Division MultiplexingOSCOscillatorPAPower AmplifierPCSPersonal Communication SystemPSKPhase Shift KeyingPSDPower Spectral DensityPARPeak-to-Average RatioQAMQudrature Amplitude ModulationRFRadio FrequencySNRSignal-to-Noise RatioSNDRSignal-to-Noise and Distortion RatioTHDTotal Harmonic DistortionUMTSUniversal Mobile Telephone SystemULUplinkVGAVariable Gain AmplifierVNAVector Network AnalyzerVSGVector Signal GeneratorVSAVector Signal AnalyzerWLANWireless Local Area NetworkWMANWireless Metropolitan NetworkWCDMAWide Band Code Division Multiple AccessWSSWide Sense Stationary