Nonlinear Modeling Analysis and Predistortion Algorithm Research of Radio Frequency Power Amplifiers

✍ Scribed by Jingchang Nan, Mingming Gao

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 379
• Edition: 1
• Category: Library

✦ Synopsis

This book is a summary of a series of achievements made by the authors and colleagues in the areas of radio frequency power amplifier modeling (including neural Volterra series modeling, neural network modeling, X-parameter modeling), nonlinear analysis methods, and power amplifier predistortion technology over the past 10 years.

The book is organized into ten chapters, which respectively describe an overview of research of power amplifier behavioral models and predistortion technology, nonlinear characteristics of power amplifiers, power amplifier behavioral models and the basis of nonlinear analysis, an overview of power amplifier predistortion, Volterra series modeling of power amplifiers, power amplifier modeling based on neural networks, power amplifier modeling with X-parameters, the modeling of other power amplifiers, nonlinear circuit analysis methods, and predistortion algorithms and applications.

Blending theory with analysis, this book will provide researchers and RF/microwave engineering students with a valuable resource.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Chapter 1. Overview of Research Status
1.1 Research Status and Development of the Behavioral Model for PA
1.2 Research Status and Development of Predistortion Technology
References
Chapter 2. Nonlinear Characteristics of Power Amplifier
2.1 Nonlinearity of Power Amplifier
2.1.1 Harmonic Distortion
2.1.2 Intermodulation Distortion
2.1.3 AM/AM and AM/PM Distortion
2.2 Memory Effects of Power Amplifier
2.2.1 Causes of Memory Effects
2.2.2 Methods to Eliminate Memory Effects
2.3 Impact of Power Amplifier Nonlinearity on Communication Systems
2.3.1 ACPR
2.3.2 EVM
References
Chapter 3. Power Amplifier Behavioral Model and Nonlinear Analysis Basis
3.1 Memoryless Behavioral Model
3.2 Memory Behavioral Model
3.2.1 Volterra Series Model and Memory Polynomial Model
3.2.2 Hammerstein Model and Wiener Model
3.2.3 Neural Network Model
3.2.4 Input-Output Relationship of Nonlinear Power Amplifier
3.2.5 Support Vector Machine Model
3.2.6 X-Parameter Model
3.2.7 Dynamic X-Parameter Theory
3.3 Theoretical Basis of Nonlinear Circuit Analysis Method
3.3.1 Harmonic Balance Method
3.3.2 Quasi-Newton Method
3.3.3 Ant Colony Algorithm
3.3.4 Bee Colony Algorithm
References
Chapter 4. Overview of Power Amplifier Predistortion
4.1 Principle and Classification of Predistortion Technology
4.1.1 Principle of Predistortion Technology
4.1.2 Classification of Predistortion Technology
4.2 Mainstream Techniques of Digital Predistortion
4.2.1 LUT and Polynomial Predistortion
4.2.2 Adaptive Learning Structure
References
Chapter 5. Volterra Series Modeling for Power Amplifier
5.1 Analysis and Buildup of Expanded Volterra Model for Nonlinear Power Amplifier with Memory Effects
5.1.1 Volterra-Chebyshev Model Derivation and Analysis
5.1.2 Volterra-Laguerre Model Analysis and Derivation
5.1.3 Model Simulation Experiment
5.2 PGSC Modeling and Digital Predistortion of Wideband Power Amplifier
5.2.1 Novel PGSC Behavioral Model Analysis
5.2.2 PGSC Model Identification
5.2.3 Test Result
5.3 LMEC Research and Predistortion Application
5.3.1 LMEC Behavioral Model Description
5.3.2 Model Identification
5.3.3 Model Performance Evaluation
5.3.4 Predistortion Application
5.4 Improved Dynamic Memory Polynomial Model of Power Amplifier and Predistortion Application
5.4.1 Improved Multi-Slice Combined Behavioral Model of Power Amplifier
5.4.2 Power Amplifier Model Evaluation and Validation
5.4.3 Predistortion Application
5.5 Research on Split Augmented Hammerstein Model
5.5.1 Model Analysis
5.5.2 Power Amplifier Design and Parameter Extraction
5.5.3 Model Simulation Experiment
5.6 Novel Hammerstein Dynamic Nonlinear Power Amplifier Model and Predistortion Application
5.6.1 Improved Hammerstein Model
5.6.2 Model Simulation and Validation
References
Chapter 6. Power Amplifier Modeling Based on Neural Network
6.1 Research on Behavioral Model of RF Power Amplifier Based on RBF Neural Network
6.1.1 RBF Neural Network Structure and Learning Algorithm
6.1.2 Power Amplifier Modeling Based on RBF Neural Network
6.2 Research on Behavioral Model of RF Power Amplifier Based on BP-RBF Neural Network
6.2.1 Theoretical Analysis of Three Models
6.2.2 3G Power Amplifier Design and Data Extraction
6.2.3 Simulation Experiment of Three Models
6.3 Fuzzy Neural Network Modeling with Improved Simplified Particle Swarm Optimization
6.3.1 Power Amplifier Model Based on Fuzzy Neural Network
6.3.2 Improved Particle Swarm Optimization
6.3.3 Power Amplifier Modeling Simulation Analysis
6.4 Fuzzy Wavelet Neural Network Modeling Based on Improved Particle Swarm Optimization
6.4.1 Adaptive Fuzzy Wavelet Neural Network
6.4.2 Improved Particle Swarm Optimization
6.4.3 Power Amplifier Modeling and Simulation
6.5 PSO-IOIF-Elman Neural Network Modeling Based on Rough Set Theory
6.5.1 OIF-Elman Neural Network Model
6.5.2 OIF-Elman Neural Network with Simplified PSO
6.5.3 Correction on Predicted Values of Power Amplifier Based on Rough Set Theory
6.5.4 Power Amplifier Modeling Simulation and Results
6.6 Neural Network Inverse Modeling Method and Applications
6.6.1 Inverse Modeling Method
6.6.2 Update Algorithm
6.6.3 Application Examples and Simulation Analysis
References
Chapter 7. Power Amplifier Modeling with X-Parameters
7.1 Design of Wideband Power Amplifier Based on X-Parameter Transistor Model
7.1.1 Extraction of X-Parameters
7.1.2 X-Parameter Model Description
7.1.3 Load-Independent X-Parameter Extraction Method
7.1.4 Wideband Power Amplifier Design
7.1.5 Simulation and Testing
7.2 Research on Dynamic X-Parameter Model Based on Memory Effects of Power Amplifier
7.2.1 Dynamic X-Parameter Theory
7.2.2 Improved Dynamic X-Parameter Model
7.2.3 Kernel Function Extraction of New Model
7.2.4 Simulation and Data Analysis
References
Chapter 8. Other Power Amplifier Modeling
8.1 Power Amplifier Model Based on Dynamic Rational Function and Predistortion Applications
8.1.1 Model Analysis
8.1.2 Model Determination and Coefficient Extraction
8.1.3 Model Performance Evaluation
8.1.4 Predistortion Application
8.2 RF Power Amplifier Model Based on Particle Swarm Optimization (PSO)_Support Vector Machine (SVM)
8.2.1 SVM and PSO
8.2.2 Simulation Experiment and Result Analysis
References
Chapter 9. Nonlinear Circuit Analysis Methods
9.1 Application of Hybrid Genetic Algorithm with Volterra Series-Based Improvement in Harmonic Balance
9.1.1 Harmonic Balance Theory
9.1.2 Improved Hybrid Genetic Algorithm
9.1.3 Simulation and Data Analysis
9.2 Application of Quasi-Newtonian Particle Swarm Optimization Algorithm in Harmonic Balance Equations for Nonlinear Circuits
9.2.1 Harmonic Balance Theory
9.2.2 Quasi-Newtonian PSO Algorithm
9.2.3 Experimental Simulation Analysis
9.3 Application of Hybrid Ant Colony Algorithm in Nonlinear Harmonic Balance Analysis
9.3.1 Fundamentals of Harmonic Balance
9.3.2 Hybrid Ant Colony Algorithm
9.3.3 Experimental Simulation Analysis
References
Chapter 10. Predistortion Algorithms and Applications
10.1 Theoretical Analysis and Simulation Implementation of Digital Baseband Predistortion for Power Amplifier
10.1.1 Digital Baseband Predistortion Structure
10.1.2 Theoretical Derivation of Transfer Function for Digital Predistorter
10.1.3 Simulation Implementation of Digital Baseband Predistortion
10.2 Research on Digital Predistortion Method of Double-Loop Structure
10.2.1 Predistortion Structure of Double-Loop Structure
10.2.2 Experimental Validation and Result Analysis
10.3 Application of Peak-to-Average Ratio Suppression and Predistortion in OFDM-ROF System
10.3.1 OFDM-ROF System Analysis
10.3.2 Nonlinear Distortion Analysis of OFDM-ROF System
10.3.3 Co-Simulation System Establishment
10.3.4 Co-Simulation Result
10.4 Combined Scheme of Peak-to-Average Ratio Suppression and Predistortion Technology with Improved Algorithm
10.4.1 System Model
10.4.2 Digital Predistortion System
10.4.3 Combined Scheme of Predistortion and Peak-to-Average Ratio Suppression
10.4.4 Experimental Result and Analysis
10.5 Sparse Normalized Power Amplifier Model and Predistortion Application
10.5.1 Model Description
10.5.2 Model Sparsification and Identification
10.5.3 Model Performance Validation
10.5.4 Predistortion Application
10.6 Combined Predistortion Method of Simplified Filter Look-Up Table and Neural Network
10.6.1 Filter Look-Up Table Predistortion
10.6.2 Predistortion Scheme Combining Improved Filter Look-Up Table and Neural Network
10.6.3 Experimental Result and Analysis
10.7 Adaptive Predistortion Method with Offline Training Based on BP Inverse Model
10.7.1 Adaptive Predistortion Method with Offline Training Based on BP Neural Network
10.7.2 Experiment and Comparative Analysis
10.8 Power Amplifier Predistortion Method Based on Adaptive Fuzzy Neural Network
10.8.1 Fuzzy Neural Network Model Structure
10.8.2 New Method for Adaptive Predistortion
10.8.3 Experimental Validation Analysis
References