{"product_id":"nonlinear-distortion-in-wireless-systems-isbn-9780470661048","title":"Nonlinear Distortion in Wireless Systems","description":"\u003cb\u003eThis book covers the principles of modeling and simulation of nonlinear distortion in wireless communication systems with MATLAB simulations and techniques\u003c\/b\u003e \u003cp\u003eIn 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.\u003c\/p\u003e \u003cp\u003eKey Features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eCovers the theory, models and simulation tools needed for understanding nonlinearity and nonlinear distortion in wireless systems\u003c\/li\u003e \u003cli\u003ePresents simulation and modeling techniques for nonlinear distortion in wireless channels using MATLAB\u003c\/li\u003e \u003cli\u003eUses random process theory to develop simulation tools for predicting nonlinear system performance with real-world wireless communication signals\u003c\/li\u003e \u003cli\u003eFocuses on simulation examples of real-world communication systems under nonlinearity\u003c\/li\u003e \u003cli\u003eIncludes an accompanying website containing MATLAB code\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis 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.\u003c\/p\u003e  \u003cb\u003ePreface xv\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eList of Abbreviations xvii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eList of Figures xix\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eList of Tables xxvii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAcknowledgements xxix\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Nonlinearity in Wireless Communication Systems 1\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.1 Power Amplifiers\u003c\/i\u003e 2\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.2 Low-Noise Amplifiers (LNAs)\u003c\/i\u003e 4\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.3 Mixers\u003c\/i\u003e 6\u003c\/p\u003e \u003cp\u003e1.2 Nonlinear Distortion in Wireless Systems 6\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.2.1 Adjacent-Channel Interference\u003c\/i\u003e 8\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.2.2 Modulation Quality and Degradation of System Performance\u003c\/i\u003e 9\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.2.3 Receiver Desensitization and Cross-Modulation\u003c\/i\u003e 11\u003c\/p\u003e \u003cp\u003e1.3 Modeling and Simulation of Nonlinear Systems 12\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.3.1 Modeling and Simulation in Engineering\u003c\/i\u003e 12\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.3.2 Modeling and Simulation for Communication System Design\u003c\/i\u003e 14\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.3.3 Behavioral Modeling of Nonlinear Systems\u003c\/i\u003e 15\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.3.4 Simulation of Nonlinear Circuits\u003c\/i\u003e 16\u003c\/p\u003e \u003cp\u003e1.4 Organization of the Book 19\u003c\/p\u003e \u003cp\u003e1.5 Summary 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Wireless Communication Systems, Standards and Signal Models 21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Wireless System Architecture 21\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.1.1 RF Transmitter Architectures\u003c\/i\u003e 23\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.1.2 Receiver Architecture\u003c\/i\u003e 26\u003c\/p\u003e \u003cp\u003e2.2 Digital Signal Processing in Wireless Systems 30\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.2.1 Digital Modulation\u003c\/i\u003e 31\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.2.2 Pulse Shaping\u003c\/i\u003e 37\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.2.3 Orthogonal Frequency Division Multiplexing (OFDM)\u003c\/i\u003e 39\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.2.4 Spread Spectrum Modulation\u003c\/i\u003e 41\u003c\/p\u003e \u003cp\u003e2.3 Mobile System Standards 45\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.3.1 Second-Generation Mobile Systems\u003c\/i\u003e 46\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.3.2 Third-Generation Mobile Systems\u003c\/i\u003e 48\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.3.3 Fourth-Generation Mobile Systems\u003c\/i\u003e 51\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.3.4 Summary\u003c\/i\u003e 51\u003c\/p\u003e \u003cp\u003e2.4 Wireless Network Standards 52\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.4.1 First-Generation Wireless LANs\u003c\/i\u003e 52\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.4.2 Second-Generation Wireless LANs\u003c\/i\u003e 52\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.4.3 Third-Generation Wireless Networks (WMANs)\u003c\/i\u003e 53\u003c\/p\u003e \u003cp\u003e2.5 Nonlinear Distortion in Different Wireless Standards 55\u003c\/p\u003e \u003cp\u003e2.6 Summary 56\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Modeling of Nonlinear Systems 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Analytical Nonlinear Models 60\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.1.1 General Volterra Series Model\u003c\/i\u003e 60\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.1.2 Wiener Model\u003c\/i\u003e 62\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.1.3 Single-Frequency Volterra Models\u003c\/i\u003e 63\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.1.4 The Parallel Cascade Model\u003c\/i\u003e 65\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.1.5 Wiener–Hammerstein Models\u003c\/i\u003e 66\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.1.6 Multi-Input Single-Output (MISO) Volterra Model\u003c\/i\u003e 67\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.1.7 The Polyspectral Model\u003c\/i\u003e 67\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.1.8 Generalized Power Series\u003c\/i\u003e 68\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.1.9 Memory Polynomials\u003c\/i\u003e 69\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.1.10 Memoryless Models\u003c\/i\u003e 70\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.1.11 Power-Series Model\u003c\/i\u003e 70\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.1.12 The Limiter Family of Models\u003c\/i\u003e 72\u003c\/p\u003e \u003cp\u003e3.2 Empirical Nonlinear Models 74\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.2.1 The Three-Box Model\u003c\/i\u003e 74\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.2.2 The Abuelma’ati Model\u003c\/i\u003e 75\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.2.3 Saleh Model\u003c\/i\u003e 76\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.2.4 Rapp Model\u003c\/i\u003e 76\u003c\/p\u003e \u003cp\u003e3.3 Parameter Extraction of Nonlinear Models from Measured Data 76\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.3.1 Polynomial Models\u003c\/i\u003e 77\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.3.2 Three-Box Model\u003c\/i\u003e 79\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.3.3 Volterra Series\u003c\/i\u003e 80\u003c\/p\u003e \u003cp\u003e3.4 Summary 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Nonlinear Transformation of Deterministic Signals 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Complex Baseband Analysis and Simulations 84\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.1.1 Complex Envelope of Modulated Signals\u003c\/i\u003e 85\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.1.2 Baseband Equivalent of Linear System Impulse Response\u003c\/i\u003e 89\u003c\/p\u003e \u003cp\u003e4.2 Complex Baseband Analysis of Memoryless Nonlinear Systems 90\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.2.1 Power-Series Model\u003c\/i\u003e 92\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.2.2 Limiter Model\u003c\/i\u003e 92\u003c\/p\u003e \u003cp\u003e4.3 Complex Baseband Analysis of Nonlinear Systems with Memory 94\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.3.1 Volterra Series\u003c\/i\u003e 94\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.3.2 Single-Frequency Volterra Models\u003c\/i\u003e 95\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.3.3 Wiener-Hammerstein Model\u003c\/i\u003e 96\u003c\/p\u003e \u003cp\u003e4.4 Complex Envelope Analysis with Multiple Bandpass Signals 97\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.4.1 Volterra Series\u003c\/i\u003e 97\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.4.2 Single-Frequency Volterra Models\u003c\/i\u003e 99\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.4.3 Wiener-Hammerstein Model\u003c\/i\u003e 100\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.4.4 Multi-Input Single-Output Nonlinear Model\u003c\/i\u003e 103\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.4.5 Memoryless Nonlinearity-Power-Series Model\u003c\/i\u003e 104\u003c\/p\u003e \u003cp\u003e4.5 Examples–Response of Power-Series Model to Multiple Signals 106\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.5.1 Single Tone\u003c\/i\u003e 107\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.5.2 Two-Tone Signal\u003c\/i\u003e 107\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.5.3 Single-Bandpass Signal\u003c\/i\u003e 108\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.5.4 Two-Bandpass Signals\u003c\/i\u003e 108\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.5.5 Single Tone and a Bandpass Signal\u003c\/i\u003e 109\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.5.6 Multisines\u003c\/i\u003e 110\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.5.7 Multisine Analysis Using the Generalized Power-Series Model\u003c\/i\u003e 111\u003c\/p\u003e \u003cp\u003e4.6 Summary 111\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Nonlinear Transformation of Random Signals 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Preliminaries 114\u003c\/p\u003e \u003cp\u003e5.2 Linear Systems with Stochastic Inputs 114\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.2.1 White Noise\u003c\/i\u003e 115\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.2.2 Gaussian Processes\u003c\/i\u003e 116\u003c\/p\u003e \u003cp\u003e5.3 Response of a Nonlinear System to a Random Input Signal 116\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.3.1 Power-Series Model\u003c\/i\u003e 116\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.3.2 Wiener–Hammerstein Models\u003c\/i\u003e 118\u003c\/p\u003e \u003cp\u003e5.4 Response of Nonlinear Systems to Gaussian Inputs 119\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.4.1 Limiter Model\u003c\/i\u003e 120\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.4.2 Memoryless Power-Series Model\u003c\/i\u003e 123\u003c\/p\u003e \u003cp\u003e5.5 Response of Nonlinear Systems to Multiple Random Signals 123\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.5.1 Power-Series Model\u003c\/i\u003e 124\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.5.2 Wiener–Hammerstein Model\u003c\/i\u003e 126\u003c\/p\u003e \u003cp\u003e5.6 Response of Nonlinear Systems to a Random Signal and a Sinusoid 128\u003c\/p\u003e \u003cp\u003e5.7 Summary 129\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Nonlinear Distortion 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Identification of Nonlinear Distortion in Digital Wireless Systems 132\u003c\/p\u003e \u003cp\u003e6.2 Orthogonalization of the Behavioral Model 134\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.2.1 Orthogonalization of the Volterra Series Model\u003c\/i\u003e 136\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.2.2 Orthogonalization of Wiener Model\u003c\/i\u003e 137\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.2.3 Orthogonalization of the Power-Series Model\u003c\/i\u003e 139\u003c\/p\u003e \u003cp\u003e6.3 Autocorrelation Function and Spectral Analysis of the Orthogonalized Model 140\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.3.1 Output Autocorrelation Function\u003c\/i\u003e 142\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.3.2 Power Spectral Density\u003c\/i\u003e 142\u003c\/p\u003e \u003cp\u003e6.4 Relationship Between System Performance and Uncorrelated Distortion 144\u003c\/p\u003e \u003cp\u003e6.5 Examples 146\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.5.1 Narrowband Gaussian Noise\u003c\/i\u003e 146\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.5.2 Multisines with Deterministic Phases\u003c\/i\u003e 148\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.5.3 Multisines with Random Phases\u003c\/i\u003e 152\u003c\/p\u003e \u003cp\u003e6.6 Measurement of Uncorrelated Distortion 154\u003c\/p\u003e \u003cp\u003e6.7 Summary 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Nonlinear System Figures of Merit 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Analogue System Nonlinear Figures of Merit 158\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.1.1 Intermodulation Ratio\u003c\/i\u003e 158\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.1.2 Intercept Points\u003c\/i\u003e 159\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.1.3 1-dB Compression Point\u003c\/i\u003e 160\u003c\/p\u003e \u003cp\u003e7.2 Adjacent-Channel Power Ratio (ACPR) 161\u003c\/p\u003e \u003cp\u003e7.3 Signal-to-Noise Ratio (SNR) 161\u003c\/p\u003e \u003cp\u003e7.4 CDMA Waveform Quality Factor (\u003ci\u003eρ\u003c\/i\u003e) 163\u003c\/p\u003e \u003cp\u003e7.5 Error Vector Magnitude (EVM) 163\u003c\/p\u003e \u003cp\u003e7.6 Co-Channel Power Ratio (CCPR) 164\u003c\/p\u003e \u003cp\u003e7.7 Noise-to-Power Ratio (NPR) 164\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.7.1 NPR of Communication Signals\u003c\/i\u003e 165\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.7.2 NBGN Model for Input Signal\u003c\/i\u003e 166\u003c\/p\u003e \u003cp\u003e7.8 Noise Figure in Nonlinear Systems 167\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.8.1 Nonlinear Noise Figure\u003c\/i\u003e 169\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.8.2 NBGN Model for Input Signal and Noise\u003c\/i\u003e 171\u003c\/p\u003e \u003cp\u003e7.9 Summary 173\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Communication System Models and Simulation in MATLAB\u003c\/b\u003e® \u003cb\u003e175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Simulation of Communication Systems 176\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.1.1 Random Signal Generation\u003c\/i\u003e 176\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.1.2 System Models\u003c\/i\u003e 176\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.1.3 Baseband versus Passband Simulations\u003c\/i\u003e 177\u003c\/p\u003e \u003cp\u003e8.2 Choosing the Sampling Rate in MATLAB® Simulations 178\u003c\/p\u003e \u003cp\u003e8.3 Random Signal Generation in MATLAB® 178\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.3.1 White Gaussian Noise Generator\u003c\/i\u003e 178\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.3.2 Random Matrices\u003c\/i\u003e 179\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.3.3 Random Integer Matrices\u003c\/i\u003e 179\u003c\/p\u003e \u003cp\u003e8.4 Pulse-Shaping Filters 180\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.4.1 Raised Cosine Filters\u003c\/i\u003e 180\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.4.2 Gaussian Filters\u003c\/i\u003e 182\u003c\/p\u003e \u003cp\u003e8.5 Error Detection and Correction 183\u003c\/p\u003e \u003cp\u003e8.6 Digital Modulation in MATLAB® 184\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.6.1 Linear Modulation\u003c\/i\u003e 184\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.6.2 Nonlinear Modulation\u003c\/i\u003e 186\u003c\/p\u003e \u003cp\u003e8.7 Channel Models in MATLAB® 188\u003c\/p\u003e \u003cp\u003e8.8 Simulation of System Performance in MATLAB® 188\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.8.1 BER\u003c\/i\u003e 190\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.8.2 Scatter Plots\u003c\/i\u003e 195\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.8.3 Eye Diagrams\u003c\/i\u003e 196\u003c\/p\u003e \u003cp\u003e8.9 Generation of Communications Signals in MATLAB® 198\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.9.1 Narrowband Gaussian Noise\u003c\/i\u003e 198\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.9.2 OFDM Signals\u003c\/i\u003e 199\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.9.3 DS-SS Signals\u003c\/i\u003e 203\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.9.4 Multisine Signals\u003c\/i\u003e 206\u003c\/p\u003e \u003cp\u003e8.10 Example 210\u003c\/p\u003e \u003cp\u003e8.11 Random Signal Generation in Simulink® 211\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.11.1 Random Data Sources\u003c\/i\u003e 211\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.11.2 Random Noise Generators\u003c\/i\u003e 212\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.11.3 Sequence Generators\u003c\/i\u003e 213\u003c\/p\u003e \u003cp\u003e8.12 Digital Modulation in Simulink® 214\u003c\/p\u003e \u003cp\u003e8.13 Simulation of System Performance in Simulink® 214\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.13.1 Example 1: Random Sources and Modulation\u003c\/i\u003e 216\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.13.2 Example 2: CDMA Transmitter\u003c\/i\u003e 217\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.13.3 Simulation of Wireless Standards in Simulink\u003c\/i\u003e® 220\u003c\/p\u003e \u003cp\u003e8.14 Summary 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Simulation of Nonlinear Systems in MATLAB\u003c\/b\u003e® \u003cb\u003e221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Generation of Nonlinearity in MATLAB® 221\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.1.1 Memoryless Nonlinearity\u003c\/i\u003e 221\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.1.2 Nonlinearity with Memory\u003c\/i\u003e 222\u003c\/p\u003e \u003cp\u003e9.2 Fitting a Nonlinear Model to Measured Data 224\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.2.1 Fitting a Memoryless Polynomial Model to Measured Data\u003c\/i\u003e 224\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.2.2 Fitting a Three-Box Model to Measured Data\u003c\/i\u003e 228\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.2.3 Fitting a Memory Polynomial Model to a Simulated Nonlinearity\u003c\/i\u003e 234\u003c\/p\u003e \u003cp\u003e9.3 Autocorrelation and Spectrum Estimation 235\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.3.1 Estimation of the Autocorrelation Function\u003c\/i\u003e 235\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.3.2 Plotting the Signal Spectrum\u003c\/i\u003e 237\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.3.3 Power Measurements from a PSD\u003c\/i\u003e 239\u003c\/p\u003e \u003cp\u003e9.4 Spectrum of the Output of a Memoryless Nonlinearity 240\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.4.1 Single Channel\u003c\/i\u003e 240\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.4.2 Two Channels\u003c\/i\u003e 243\u003c\/p\u003e \u003cp\u003e9.5 Spectrum of the Output of a Nonlinearity with Memory 246\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.5.1 Three-Box Model\u003c\/i\u003e 246\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.5.2 Memory Polynomial Model\u003c\/i\u003e 249\u003c\/p\u003e \u003cp\u003e9.6 Spectrum of Orthogonalized Nonlinear Model 251\u003c\/p\u003e \u003cp\u003e9.7 Estimation of System Metrics from Simulated Spectra 256\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.7.1 Signal-to-Noise and Distortion Ratio (SNDR)\u003c\/i\u003e 257\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.7.2 EVM\u003c\/i\u003e 260\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.7.3 ACPR\u003c\/i\u003e 262\u003c\/p\u003e \u003cp\u003e9.8 Simulation of Probability of Error 263\u003c\/p\u003e \u003cp\u003e9.9 Simulation of Noise-to-Power Ratio 268\u003c\/p\u003e \u003cp\u003e9.10 Simulation of Nonlinear Noise Figure 271\u003c\/p\u003e \u003cp\u003e9.11 Summary 278\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Simulation of Nonlinear Systems in Simulink\u003c\/b\u003e® \u003cb\u003e279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 RF Impairments in Simulink® 280\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.1.1 Communications Blockset\u003c\/i\u003e 280\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.1.2 The RF Blockset\u003c\/i\u003e 280\u003c\/p\u003e \u003cp\u003e10.2 Nonlinear Amplifier Mathematical Models in Simulink® 283\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.2.1 The “Memoryless Nonlinearity” Block-Communications Blockset\u003c\/i\u003e 283\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.2.2 Cubic Polynomial Model\u003c\/i\u003e 284\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.2.3 Hyperbolic Tangent Model\u003c\/i\u003e 284\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.2.4 Saleh Model\u003c\/i\u003e 285\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.2.5 Ghorbani Model\u003c\/i\u003e 285\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.2.6 Rapp Model\u003c\/i\u003e 285\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.2.7 Example\u003c\/i\u003e 286\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.2.8 The “Amplifier” Block–The RF Blockset\u003c\/i\u003e 286\u003c\/p\u003e \u003cp\u003e10.3 Nonlinear Amplifier Physical Models in Simulink® 289\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.3.1 “General Amplifier” Block\u003c\/i\u003e 290\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.3.2 “S-Parameter Amplifier” Block\u003c\/i\u003e 296\u003c\/p\u003e \u003cp\u003e10.4 Measurements of Distortion and System Metrics 297\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.4.1 Adjacent-Channel Distortion\u003c\/i\u003e 297\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.4.2 In-Band Distortion\u003c\/i\u003e 297\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.4.3 Signal-to-Noise and Distortion Ratio\u003c\/i\u003e 300\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.4.4 Error Vector Magnitude\u003c\/i\u003e 300\u003c\/p\u003e \u003cp\u003e10.5 Example: Performance of Digital Modulation with Nonlinearity 301\u003c\/p\u003e \u003cp\u003e10.6 Simulation of Noise-to-Power Ratio 302\u003c\/p\u003e \u003cp\u003e10.7 Simulation of Noise Figure in Nonlinear Systems 304\u003c\/p\u003e \u003cp\u003e10.8 Summary 306\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Basics of Signal and System Analysis 307\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Signals 308\u003c\/p\u003e \u003cp\u003eA.2 Systems 308\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Random Signal Analysis 311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Random Variables 312\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.1.1 Examples of Random Variables\u003c\/i\u003e 312\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.1.2 Functions of Random Variables\u003c\/i\u003e 312\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.1.3 Expectation\u003c\/i\u003e 313\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.1.4 Moments\u003c\/i\u003e 314\u003c\/p\u003e \u003cp\u003eB.2 Two Random Variables 314\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.2.1 Independence\u003c\/i\u003e 315\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.2.2 Joint Statistics\u003c\/i\u003e 315\u003c\/p\u003e \u003cp\u003eB.3 Multiple Random Variables 316\u003c\/p\u003e \u003cp\u003eB.4 Complex Random Variables 317\u003c\/p\u003e \u003cp\u003eB.5 Gaussian Random Variables 318\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.5.1 Single Gaussian Random Variable\u003c\/i\u003e 318\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.5.2 Moments of Single Gaussian Random Variable\u003c\/i\u003e 319\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.5.3 Jointly Gaussian Random Variables\u003c\/i\u003e 319\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.5.4 Price’s Theorem\u003c\/i\u003e 320\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.5.5 Multiple Gaussian Random Variable\u003c\/i\u003e 320\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.5.6 Central Limit Theorem\u003c\/i\u003e 321\u003c\/p\u003e \u003cp\u003eB.6 Random Processes 321\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.6.1 Stationarity\u003c\/i\u003e 322\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.6.2 Ergodicity\u003c\/i\u003e 323\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.6.3 White Processes\u003c\/i\u003e 323\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.6.4 Gaussian Processes\u003c\/i\u003e 324\u003c\/p\u003e \u003cp\u003eB.7 The Power Spectrum 324\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.7.1 White Noise Processes\u003c\/i\u003e 325\u003c\/p\u003e \u003cp\u003e\u003ci\u003eB.7.2 Narrowband Processes\u003c\/i\u003e 326\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C Introduction to MATLAB\u003c\/b\u003e® \u003cb\u003e329\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eC.1 MATLAB® Scripts 329\u003c\/p\u003e \u003cp\u003eC.2 MATLAB® Structures 330\u003c\/p\u003e \u003cp\u003eC.3 MATLAB® Graphics 330\u003c\/p\u003e \u003cp\u003eC.4 Random Number Generators 330\u003c\/p\u003e \u003cp\u003eC.5 Moments and Correlation Functions of Random Sequences 332\u003c\/p\u003e \u003cp\u003eC.6 Fourier Transformation 332\u003c\/p\u003e \u003cp\u003eC.7 MATLAB® Toolboxes 333\u003c\/p\u003e \u003cp\u003e\u003ci\u003eC.7.1 The Communication Toolbox\u003c\/i\u003e 334\u003c\/p\u003e \u003cp\u003e\u003ci\u003eC.7.2 The RF Toolbox\u003c\/i\u003e 334\u003c\/p\u003e \u003cp\u003eC.8 Simulink® 335\u003c\/p\u003e \u003cp\u003e\u003ci\u003eC.8.1 The Communication Blockset\u003c\/i\u003e 339\u003c\/p\u003e \u003cp\u003e\u003ci\u003eC.8.2 The RF Blockset\u003c\/i\u003e 339\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences 341\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex 347\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e“It is appropriate for professionals or graduate students.”  (\u003ci\u003eBook News\u003c\/i\u003e, 1 April 2012)\u003c\/p\u003e \u003cb\u003eKhaled M. Gharaibeh, Yarmouk University, Jordan\u003c\/b\u003e\u003cbr\u003eKhaled 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.","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989695185125,"sku":"NP9780470661048","price":148.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470661048.jpg?v=1761785138","url":"https:\/\/k12savings.com\/products\/nonlinear-distortion-in-wireless-systems-isbn-9780470661048","provider":"K12savings","version":"1.0","type":"link"}