Covers theoretical and practical aspects related to the behavioral modelling and predistortion of wireless transmitters and power amplifiers. It includes simulation software that enables the users to apply the theory presented in the book. In the first section, the reader is given the general background of nonlinear dynamic systems along with their behavioral modelling from all its aspects. In the second part, a comprehensive compilation of behavioral models formulations and structures is provided including memory polynomial based models, box oriented models such as Hammerstein-based and Wiener-based models, and neural networks-based models. The book will be a valuable resource for design engineers, industrial engineers, applications engineers, postgraduate students, and researchers working on power amplifiers modelling, linearization, and design.

Covers theoretical and practical aspects related to the behavioral modelling and predistortion of wireless transmitters and power amplifiers. It includes simulation software that enables the users to apply the theory presented in the book. In the first section, the reader is given the general background of nonlinear dynamic systems along with their behavioral modelling from all its aspects. In the second part, a comprehensive compilation of behavioral models formulations and structures is provided including memory polynomial based models, box oriented models such as Hammerstein-based and Wiener-based models, and neural networks-based models. The book will be a valuable resource for design engineers, industrial engineers, applications engineers, postgraduate students, and researchers working on power amplifiers modelling, linearization, and design.

PrefaceChapter 1: Characterization of Wireless Transmitter Distortions1.1 Introduction1.1.1 RF Power Amplifiers Nonlinearity1.1.2 Inter-modulation Distortion and Spectrum Regrowth1.2 Impact of the Distortions on Transmitter Performances1.3 Output Power versus Input Power Characteristic1.4 AM/AM and AM/PM Characteristics1.5 1dB Compression Point1.6 Third and Fifth Order Intercept Points1.7 Carrier to Inter-Modulation Distortion Ratio1.8 Adjacent Channel Leakage Ratio1.9 Error Vector MagnitudeReferencesChapter 2: Dynamic Nonlinear Systems2.1 Classification of Nonlinear Systems2.1.1 Memoryless Systems2.1.2 Systems with Memory2.2 Memory in Microwave Power Amplification Systems2.2.1 Nonlinear Systems without Memory2.2.2 Weakly nonlinear and Quasi-Memoryless Systems2.2.3 Nonlinear System with Memory2.3 Baseband and Low-Pass Equivalent Signals2.4 Origins and Types of Memory Effects in Power Amplification Systems2.4.1 Origins of Memory Effects2.4.2 Electrical Memory Effects2.4.3 Thermal Memory Effects2.5 Volterra Series ModelsReferencesChapter 3: Model Performance Evaluation3.1 Introduction3.2 Behavioral Modeling vs Digital Predistortion3.3 Time Domain Metrics3.3.1 Normalized Mean Square Error3.3.2 Memory Effects Modeling Ratio3.4 Frequency Domain Metrics3.4.1 Frequency Domain Normalized Mean Square Error3.4.2 Adjacent Channel Error Power Ratio3.4.3 Weighted Error Spectrum Power Ratio3.4.4 Normalized Absolute Mean Spectrum Error3.5 Static Nonlinearity Cancellation Techniques3.5.1 Static Nonlinearity Pre-Compensation Technique3.5.2 Static Nonlinearity Post-Compensation Technique3.5.3 Memory Effects Intensity3.6 Discussion and ConclusionReferencesChapter 4: Quasi-Memoryless Behavior Models4.1 Introduction4.2 Modeling and Simulation of Memoryless/Quasi-Memoryless Nonlinear Systems4.3 Bandpass to Baseband Equivalent Transformation4.4 Look-up Table Models4.4.1 Non-uniform Indexed Look-up Tables4.5 Empirical Analytical Based Models4.5.1 Class AB Amplifier Behavior Model4.6 Saleh Based Models4.6.1 Polar Saleh Model4.6.2 Cartesian Saleh Model4.6.3 Frequency-dependent Saleh Model4.6.4 Ghorbani Model4.6.5 Berman & Mahle Phase Model4.6.6 Thomas-Weidner-Durrani Amplitude Model4.6.7 Limiter Model4.6.8 ARCTAN Model4.6.9 Rapp Model4.6.10 White Model4.7 Power Series Models4.7.1 Polynomial Model4.7.2 Bessel Function Based Model4.7.3 Chebyshev Series Based Model4.7.4 Gegenbauer Polynomials Based Model4.7.5 Zernike Polynomials Based ModelReferencesChapter 5: Memory Polynomial Based Models5.1 Introduction5.2 Generic Memory Polynomial Model Formulation5.3 Memory Polynomial Model5.4 Variants of the Memory Polynomial Model5.4.1 Orthogonal Memory Polynomial Model5.4.2 Sparse-Delay Memory Polynomial Model5.4.3 Exponentially Shaped Memory Delay Profile Memory Polynomial Model5.4.4 Non-uniform Memory Polynomial Model5.4.5 Unstructured Memory Polynomial Model5.5 Envelope Memory Polynomial Model5.6 Generalized Memory Polynomial Model5.7 Hybrid Memory Polynomial Model5.8 Dynamic Deviation Reduction Volterra Model5.9 Comparison and DiscussionReferencesChapter 6: Box-Oriented Models6.1 Introduction6.2 Hammerstein and Wiener Models6.2.1 Wiener Model6.2.2 Hammerstein Model6.3 Augmented Hammerstein and Weiner Models6.3.1 Augmented Wiener Model6.3.2 Augmented Hammerstein Model6.4 Three-Box Wiener-Hammerstein Models6.4.1 Wiener-Hammerstein Model6.4.2 Hammerstein-Wiener Model6.4.3 Feed-Forward Hammerstein Model6.5 Two-Box Polynomial Models6.5.1 Models Description6.5.2 Identification Procedure6.6 Three-Box Polynomial Models6.6.1 Parallel Three-blocks Model - Plume Model6.6.2 Three layered biased memory polynomial Model6.6.3 Rational Function Model for Amplifiers6.7 Polynomial based Model with I/Q and DC impairments6.7.1 Parallel Hammerstein (PH) based model for the alleviation of various imperfections in Direct Conversion transmitters6.7.2 Two-Box Model with I/Q and DC ImpairmentsReferencesChapter 7: Neural Network Based Models7.1 Introduction7.2 Basics of Neural Networks7.3 Neural Networks Architecture for Modeling of Complex Static Systems7.3.1 Single-Input Single-Output Feedforward Neural Network (SISO-FFNN)7.3.2 Dual-Input Dual-Output Feedforward Neural Network (DIDO-FFNN)7.3.3 Dual-Input Dual-Output Coupled Cartesian based Neural Network (DIDO-CC-NN)7.4 Neural Networks Architectures for Modeling of Complex Dynamic Systems7.4.1 Complex Time-Delay Recurrent Neural Network (CTDRNN)7.4.2 Complex Time-Delay Neural Network (CTDNN)7.4.3 Real Valued Time-Delay Recurrent Neural Network (RVTDRNN)7.4.4 Real Valued Time-Delay Neural Network (RVTDNN)7.5 Training Algorithms7.6 ConclusionReferencesChapter 8: Characterization and Identification Techniques8.1 Introduction8.2 Test Signals for Power Amplifiers and Transmitters Characterization8.2.1 Characterization using Continuous Wave Signals8.2.2 Characterization using Two-Tone Signals8.2.3 Characterization using Multi-Tone Signals8.2.4 Characterization using Modulated Signals8.2.5 Characterization using Synthetic Modulated Signals8.2.6 Discussion: Impact of Test Signal on the Measured AM/AM and AM/PM Characteristics8.3 Data De-embedding in Modulated Signals Based Characterization8.4 Identification Techniques8.4.1 Moving average Techniques8.4.2 Model Coefficient Extraction Techniques8.5 Robustness of System Identification Algorithms8.5.1 The LS Algorithm8.5.2 The LMS Algorithm8.5.3 The RLS Algorithm8.6 ConclusionsReferencesChapter 9: Baseband Digital Predistortion9.1 The Predistortion Concept9.2 Adaptive Digital Predistortion9.2.1 Closed Loop Adaptive Digital Predistorters9.2.2 Open Loop Adaptive Digital Predistorters9.3 The Predistorter's Power Range in Indirect Learning Architectures9.3.1 Constant Peak Power Technique9.3.2 Constant Average Power Technique9.3.3 Synergetic CFR and DPD Technique9.4 Small Signal Gain Normalization9.5 Digital Predistortion Implementations9.5.1 Baseband Digital Predistortion9.5.2 RF Digital Predistortion9.6 The Bandwidth and Power Scalable Digital Predistortion TechniqueReferencesChapter 10: Advanced Modeling and Digital Predistortion10.1 Joint Quadrature Impairment and Nonlinear Distortion Compensation10.1.1 Modeling of Quadrature Modulator Imperfections10.1.2 Dual-Input Polynomial Model for Memoryless Joint Modeling of Quadrature Imbalance and PA Distortions10.1.3 Dual-Input Memory Polynomial for Joint Modeling of Quadrature Imbalance and PA Distortions Including Memory Effects10.1.4 Dual-Branch Parallel Hammerstein Model for Joint Modeling of Quadrature Imbalance and PA Distortions with Memory10.1.5 Dual-Conjugate-Input Memory Polynomial for Joint Modeling of Quadrature Imbalance and PA Distortions Including Memory Effects10.2 Modelling and Linearization of Nonlinear MIMO Systems10.2.1 Impairments in MIMO Systems10.2.2 Crossover Polynomial Model for MIMO Transmitters10.2.3 Dual-Input Nonlinear Polynomial Model for MIMO Transmitters10.2.4 MIMO Transmitters Nonlinear Multi-variable Polynomial Model10.3 Modelling and Linearization of Dual Band Transmitters10.3.1 Generalization of the Polynomial Model to Dual-Band Case10.3.2 Two-Dimensional (2-D) Memory Polynomial Model for Dual-Band Transmitters10.3.3 Phase-Aligned Multi-band Volterra DPD10.4 Application of MIMO and Dual-band Models in Digital Predisortion10.4.1 Linearization of MIMO Systems with Nonlinear Crosstalk10.4.2 Linearization of Concurrent Dual-Band Transmitters using 2D Memory Polynomial Model10.4.3 Linearization of Concurrent Tri-Band Transmitters using 3D Phase-Aligned Volterra Model10.5 ReferencesIndex