ISBN-10:
0470661046
ISBN-13:
9780470661048
Pub. Date:
01/11/2012
Publisher:
Wiley
Nonlinear Distortion in Wireless Systems: Modeling and Simulation with MATLAB / Edition 1

Nonlinear Distortion in Wireless Systems: Modeling and Simulation with MATLAB / Edition 1

by Khaled M. Gharaibeh
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Product Details

ISBN-13: 9780470661048
Publisher: Wiley
Publication date: 01/11/2012
Series: Wiley - IEEE Series
Pages: 386
Product dimensions: 6.80(w) x 9.90(h) x 0.90(d)

About the Author

Khaled M. Gharaibeh, Yarmouk University, Jordan
Khaled M. Gharaibeh received his B.S. and M.S. in Electrical Engineering in 1995 and 1998, respectively, both from Jordan University of Science and Technology, Irbid, Jordan. He received his Ph.D. in Electrical Engineering from North Carolina State University in 2004. From 1996 to 2000, he was a planning Engineer at Jordan Telecom, Amman, Jordan. From January 2004 to 2005, he was a research associate post-doctorate at the Department Electrical and Computer Engineering, North Carolina State University. Currently he is an Assistant Professor of Electrical Engineering at the Hijawi faculty for Engineering Technology of Yarmouk University, Irbid, Jordan. His research interests are in nonlinear system identification, behavioural modelling of nonlinear RF circuits and wireless communications. He is a senior member of the Institute of Electrical and Electronics Engineering (IEEE) and the honour society Eta Kappa Nu.

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Table of Contents

Preface

List of Abbreviations

List of Figures

List of Tables

Acknowledgements

1 Introduction

1.1 Nonlinearity in Wireless Communication Systems

1.1.1 Power Amplifiers

1.1.2 Low-Noise Amplifiers (LNAs)

1.1.3 Mixers

1.2 Nonlinear Distortion in Wireless Systems

1.2.1 Adjacent-Channel Interference

1.2.2 Modulation Quality and Degradation of System Performance

1.2.3 Receiver Desensitization and Cross-Modulation

1.3 Modeling and Simulation of Nonlinear Systems

1.3.1 Modeling and Simulation in Engineering

1.3.2 Modeling and Simulation for Communication System Design

1.3.3 Behavioral Modeling of Nonlinear Systems

1.3.4 Simulation of Nonlinear Circuits

1.4 Organization of the Book

1.5 Summary

2 Wireless Communication Systems, Standards and Signal Models

2.1 Wireless System Architecture

2.1.1 RF Transmitter Architectures

2.1.2 Receiver Architecture

2.2 Digital Signal Processing in Wireless Systems

2.2.1 Digital Modulation

2.2.2 Pulse Shaping

2.2.3 Orthogonal Frequency Division Multiplexing (OFDM)

2.2.4 Spread Spectrum Modulation

2.3 Mobile System Standards

2.3.1 Second-Generation Mobile Systems

2.3.2 Third-Generation Mobile Systems

2.3.3 Fourth-Generation Mobile Systems

2.3.4 Summary

2.4 Wireless Network Standards

2.4.1 First-Generation Wireless LANs

2.4.2 Second-Generation Wireless LANs

2.4.3 Third-Generation Wireless Networks (WMANs)

2.5 Nonlinear Distortion in Different Wireless Standards

2.6 Summary

3 Modeling of Nonlinear Systems

3.1 Analytical Nonlinear Models

3.1.1 General Volterra Series Model

3.1.2 Wiener Model

3.1.3 Single-Frequency Volterra Models

3.1.4 The Parallel Cascade Model

3.1.5 Wiener–Hammerstein Models

3.1.6 Multi-Input Single-Output (MISO) Volterra Model

3.1.7 The Polyspectral Model

3.1.8 Generalized Power Series

3.1.9 Memory Polynomials

3.1.10 Memoryless Models

3.1.11 Power-Series Model

3.1.12 The Limiter Family of Models

3.2 Empirical Nonlinear Models

3.2.1 The Three-Box Model

3.2.2 The Abuelma’ati Model

3.2.3 Saleh Model

3.2.4 Rapp Model

3.3 Parameter Extraction of Nonlinear Models from Measured Data

3.3.1 Polynomial Models

3.3.2 Three-Box Model

3.3.3 Volterra Series

3.4 Summary

4 Nonlinear Transformation of Deterministic Signals

4.1 Complex Baseband Analysis and Simulations

4.1.1 Complex Envelope of Modulated Signals

4.1.2 Baseband Equivalent of Linear System Impulse Response

4.2 Complex Baseband Analysis of Memoryless Nonlinear Systems

4.2.1 Power-Series Model

4.2.2 Limiter Model

4.3 Complex Baseband Analysis of Nonlinear Systems with Memory

4.3.1 Volterra Series

4.3.2 Single-Frequency Volterra Models

4.3.3 Wiener-Hammerstein Model

4.4 Complex Envelope Analysis with Multiple Bandpass Signals

4.4.1 Volterra Series

4.4.2 Single-Frequency Volterra Models

4.4.3 Wiener-Hammerstein Model

4.4.4 Multi-Input Single-Output Nonlinear Model

4.4.5 Memoryless Nonlinearity-Power-Series Model

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

4.5.1 Single Tone

4.5.2 Two-Tone Signal

4.5.3 Single-Bandpass Signal

4.5.4 Two-Bandpass Signals

4.5.5 Single Tone and a Bandpass Signal

4.5.6 Multisines

4.5.7 Multisine Analysis Using the Generalized Power-Series

Model

4.6 Summary

5 Nonlinear Transformation of Random Signals

5.1 Preliminaries

5.2 Linear Systems with Stochastic Inputs

5.2.1 White Noise

5.2.2 Gaussian Processes

5.3 Response of a Nonlinear System to a Random Input Signal

5.3.1 Power-Series Model

5.3.2 Wiener–Hammerstein Models

5.4 Response of Nonlinear Systems to Gaussian Inputs

5.4.1 Limiter Model

5.4.2 Memoryless Power-Series Model

5.5 Response of Nonlinear Systems to Multiple Random Signals

5.5.1 Power-Series Model

5.5.2 Wiener–Hammerstein Model

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

5.7 Summary

6 Nonlinear Distortion

6.1 Identification of Nonlinear Distortion in Digital Wireless Systems

6.2 Orthogonalization of the Behavioral Model

6.2.1 Orthogonalization of the Volterra Series Model

6.2.2 Orthogonalization of Wiener Model

6.2.3 Orthogonalization of the Power-Series Model

6.3 Autocorrelation Function and Spectral Analysis

of the Orthogonalized Model

6.3.1 Output Autocorrelation Function

6.3.2 Power Spectral Density

6.4 Relationship Between System Performance and Uncorrelated Distortion

6.5 Examples

6.5.1 Narrowband Gaussian Noise

6.5.2 Multisines with Deterministic Phases

6.5.3 Multisines with Random Phases

6.6 Measurement of Uncorrelated Distortion

6.7 Summary

7 Nonlinear System Figures of Merit

7.1 Analogue System Nonlinear Figures of Merit

7.1.1 Intermodulation Ratio

7.1.2 Intercept Points

7.1.3 1-dB Compression Point

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)

7.7.1 NPR of Communication Signals

7.7.2 NBGN Model for Input Signal

7.8 Noise Figure in Nonlinear Systems

7.8.1 Nonlinear Noise Figure

7.8.2 NBGN Model for Input Signal and Noise

7.9 Summary

8 Communication System Models and Simulation in MATLAB®

8.1 Simulation of Communication Systems

8.1.1 Random Signal Generation

8.1.2 System Models

8.1.3 Baseband versus Passband Simulations

8.2 Choosing the Sampling Rate in MATLAB® Simulations

8.3 Random Signal Generation in MATLAB®

8.3.1 White Gaussian Noise Generator

8.3.2 Random Matrices

8.3.3 Random Integer Matrices

8.4 Pulse-Shaping Filters

8.4.1 Raised Cosine Filters

8.4.2 Gaussian Filters

8.5 Error Detection and Correction

8.6 Digital Modulation in MATLAB®

8.6.1 Linear Modulation

8.6.2 Nonlinear Modulation

8.7 Channel Models in MATLAB®

8.8 Simulation of System Performance in MATLAB®

8.8.1 BER

8.8.2 Scatter Plots

8.8.3 Eye Diagrams

8.9 Generation of Communications Signals in MATLAB

8.9.1 Narrowband Gaussian Noise

8.9.2 OFDM Signals

8.9.3 DS-SS Signals

8.9.4 Multisine Signals

8.10 Example

8.11 Random Signal Generation in Simulink®

8.11.1 Random Data Sources

8.11.2 Random Noise Generators

8.11.3 Sequence Generators

8.12 Digital Modulation in Simulink®

8.13 Simulation of System Performance in Simulink®

8.13.1 Example 1: Random Sources and Modulation

8.13.2 Example 2: CDMA Transmitter

8.13.3 Simulation of Wireless Standards in Simulink®

8.14 Summary

9 Simulation of Nonlinear Systems in MATLAB

9.1 Generation of Nonlinearity in MATLAB

9.1.1 Memoryless Nonlinearity

9.1.2 Nonlinearity with Memory

9.2 Fitting a Nonlinear Model to Measured Data

9.2.1 Fitting a Memoryless Polynomial Model to Measured Data

9.2.2 Fitting a Three-Box Model to Measured Data

9.2.3 Fitting a Memory Polynomial Model

to a Simulated Nonlinearity

9.3 Autocorrelation and Spectrum Estimation

9.3.1 Estimation of the Autocorrelation Function

9.3.2 Plotting the Signal Spectrum

9.3.3 Power Measurements from a PSD

9.4 Spectrum of the Output of a Memoryless Nonlinearity

9.4.1 Single Channel

9.4.2 Two Channels

9.5 Spectrum of the Output of a Nonlinearity with Memory

9.5.1 Three-Box Model

9.5.2 Memory Polynomial Model

9.6 Spectrum of Orthogonalized Nonlinear Model

9.7 Estimation of System Metrics from Simulated Spectra

9.7.1 Signal-to-Noise and Distortion Ratio (SNDR)

9.7.2 EVM

9.7.3 ACPR

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

10 Simulation of Nonlinear Systems in Simulink®

10.1 RF Impairments in Simulink®

10.1.1 Communications Blockset

10.1.2 The RF Blockset

10.2 Nonlinear Amplifier Mathematical Models in Simulink®

10.2.1 The “Memoryless Nonlinearity” Block-Communications Blockset

10.2.2 Cubic Polynomial Model

10.2.3 Hyperbolic Tangent Model

10.2.4 Saleh Model

10.2.5 Ghorbani Model

10.2.6 Rapp Model

10.2.7 Example

10.2.8 The “Amplifier” Block–The RF Blockset

10.3 Nonlinear Amplifier Physical Models in Simulink®

10.3.1 “General Amplifier” Block

10.3.2 “S-Parameter Amplifier” Block

10.4 Measurements of Distortion and System Metrics

10.4.1 Adjacent-Channel Distortion

10.4.2 In-Band Distortion

10.4.3 Signal-to-Noise and Distortion Ratio

10.4.4 Error Vector Magnitude

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.1.1 Examples of Random Variables

B.1.2 Functions of Random Variables

B.1.3 Expectation

B.1.4 Moments

B.2 Two Random Variables

B.2.1 Independence

B.2.2 Joint Statistics

B.3 Multiple Random Variables

B.4 Complex Random Variables

B.5 Gaussian Random Variables

B.5.1 Single Gaussian Random Variable

B.5.2 Moments of Single Gaussian Random Variable

B.5.3 Jointly Gaussian Random Variables

B.5.4 Price’s Theorem

B.5.5 Multiple Gaussian Random Variable

B.5.6 Central Limit Theorem

B.6 Random Processes

B.6.1 Stationarity

B.6.2 Ergodicity

B.6.3 White Processes

B.6.4 Gaussian Processes

B.7 The Power Spectrum

B.7.1 White Noise Processes

B.7.2 Narrowband Processes

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.7.1 The Communication Toolbox

C.7.2 The RF Toolbox

C.8 Simulink®

C.8.1 The Communication Blockset

C.8.2 The RF Blockset

References Index

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