Nonlinear Modeling Analysis and Predistortion Algorithm Research of Radio Frequency Power Amplifiers 1st Edition by Jingchang Nan, Mingming Gao ISBN 9781000409598 1000409597

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ISBN 10: 1000409597
ISBN 13: 9781000409598
Author: Jingchang Nan, Mingming Gao

Nonlinear Modeling Analysis and Predistortion Algorithm Research of Radio Frequency Power Amplifiers 1st Edition Table of contents:

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

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Tags: Jingchang Nan, Mingming Gao, Nonlinear Modeling Analysis, Predistortion Algorithm, Radio Frequency