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E-raamat: Nonlinear Modeling Analysis and Predistortion Algorithm Research of Radio Frequency Power Amplifiers [Taylor & Francis e-raamat]

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  • Formaat: 360 pages, 23 Tables, black and white; 193 Line drawings, black and white; 2 Halftones, black and white; 195 Illustrations, black and white
  • Ilmumisaeg: 30-Jul-2021
  • Kirjastus: CRC Press
  • ISBN-13: 9781003176855
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  • Taylor & Francis e-raamat
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Nonlinear Modeling Analysis and Predistortion Algorithm Research of Radio Frequency Power AmplifiersSuurem pilt
  • Formaat: 360 pages, 23 Tables, black and white; 193 Line drawings, black and white; 2 Halftones, black and white; 195 Illustrations, black and white
  • Ilmumisaeg: 30-Jul-2021
  • Kirjastus: CRC Press
  • ISBN-13: 9781003176855
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003176855
"This book is a summary of a series of achievements made by the authors and colleagues in the areas of radio frequency power amplifier modelling (including neural Volterra series modelling, neural network modelling, X-parameter modelling), 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 modelling of power amplifiers, power amplifier modelling based on neural networks, power amplifier modelling with X-parameters, the modelling 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"--

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.



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.

Preface xv
Chapter 1 Overview of Research Status 1(16)
1.1 Research Status And Development Of The Behavioral Model For PA
4(8)
1.2 Research Status And Development Of Predistortion Technology
12(2)
References
14(3)
Chapter 2 Nonlinear Characteristics of Power Amplifier 17(14)
2.1 Nonlinearity Of Power Amplifier
17(5)
2.1.1 Harmonic Distortion
18(1)
2.1.2 Intermodulation Distortion
19(2)
2.1.3 AM/AM and AM/PM Distortion
21(1)
2.2 Memory Effects Of Power Amplifier
22(4)
2.2.1 Causes of Memory Effects
23(1)
2.2.2 Methods to Eliminate Memory Effects
24(2)
2.3 Impact Of Power Amplifier Nonlinearity On Communication Systems
26(3)
2.3.1 ACPR
26(1)
2.3.2 EVM
27(2)
References
29(2)
Chapter 3 Power Amplifier Behavioral Model and Nonlinear Analysis Basis 31(40)
3.1 Memoryless Behavioral Model
31(1)
3.2 Memory Behavioral Model
32(27)
3.2.1 Volterra Series Model and Memory Polynomial Model
33(4)
3.2.2 Hammerstein Model and Wiener Model
37(2)
3.2.3 Neural Network Model
39(11)
3.2.4 Input-Output Relationship of Nonlinear Power Amplifier
50(1)
3.2.5 Support Vector Machine Model
51(5)
3.2.6 X-Parameter Model
56(2)
3.2.7 Dynamic X-Parameter Theory
58(1)
3.3 Theoretical Basis Of Nonlinear Circuit Analysis Method
59(10)
3.3.1 Harmonic Balance Method
59(4)
3.3.2 Quasi-Newton Method
63(2)
3.3.3 Ant Colony Algorithm
65(2)
3.3.4 Bee Colony Algorithm
67(2)
References
69(2)
Chapter 4 Overview of Power Amplifier Predistortion 71(10)
4.1 Principle And Classification Of Predistortion Technology
71(3)
4.1.1 Principle of Predistortion Technology
71(2)
4.1.2 Classification of Predistortion Technology
73(1)
4.2 Mainstream Techniques Of Digital Predistortion
74(4)
4.2.1 LUT and Polynomial Predistortion
74(2)
4.2.2 Adaptive Learning Structure
76(2)
References
78(3)
Chapter 5 Volterra Series Modeling for Power Amplifier 81(54)
5.1 Analysis And Buildup Of Expanded Volterra Model For Nonlinear Power Amplifier With Memory Effects
82(9)
5.1.1 Volterra-Chebyshev Model Derivation and Analysis
82(5)
5.1.2 Volterra-Laguerre Model Analysis and Derivation
87(2)
5.1.3 Model Simulation Experiment
89(2)
5.2 PGSC Modeling And Digital Predistortion Of Wideband Power Amplifier
91(10)
5.2.1 Novel PGSC Behavioral Model Analysis
92(3)
5.2.2 PGSC Model Identification
95(1)
5.2.3 Test Result
96(5)
5.3 LMEC Research And Predistortion Application
101(9)
5.3.1 LMEC Behavioral Model Description
102(3)
5.3.2 Model Identification
105(1)
5.3.3 Model Performance Evaluation
106(2)
5.3.4 Predistortion Application
108(2)
5.4 Improved Dynamic Memory Polynomial Model Of Power Amplifier And Predistortion Application
110(7)
5.4.1 Improved Multi-Slice Combined Behavioral Model of Power Amplifier
111(1)
5.4.2 Power Amplifier Model Evaluation and Validation
112(2)
5.4.3 Predistortion Application
114(3)
5.5 Research On Split Augmented Hammerstein Model
117(7)
5.5.1 Model Analysis
118(2)
5.5.2 Power Amplifier Design and Parameter Extraction
120(1)
5.5.3 Model Simulation Experiment
120(4)
5.6 Novel Hammerstein Dynamic Nonlinear Power Amplifier Model And Predistortion Application
124(8)
5.6.1 Improved Hammerstein Model
124(2)
5.6.2 Model Simulation and Validation
126(6)
References
132(3)
Chapter 6 Power Amplifier Modeling Based on Neural Network 135(70)
6.1 Research On Behavioral Model Of RF Power Amplifier Based On RBF Neural Network
135(10)
6.1.1 RBF Neural Network Structure and Learning Algorithm
136(5)
6.1.2 Power Amplifier Modeling Based on RBF Neural Network
141(4)
6.2 Research On Behavioral Model Of Rf Power Amplifier Based On BP-RBF Neural Network
145(9)
6.2.1 Theoretical Analysis of Three Models
145(3)
6.2.2 3G Power Amplifier Design and Data Extraction
148(2)
6.2.3 Simulation Experiment of Three Models
150(4)
6.3 Fuzzy Neural Network Modeling With Improved Simplified Particle Swarm Optimization
154(13)
6.3.1 Power Amplifier Model Based on Fuzzy Neural Network
155(4)
6.3.2 Improved Particle Swarm Optimization
159(3)
6.3.3 Power Amplifier Modeling Simulation Analysis
162(5)
6.4 Fuzzy Wavelet Neural Network Modeling Based On Improved Particle Swarm Optimization
167(10)
6.4.1 Adaptive Fuzzy Wavelet Neural Network
168(3)
6.4.2 Improved Particle Swarm Optimization
171(2)
6.4.3 Power Amplifier Modeling and Simulation
173(4)
6.5 PSO-IOIF-ELMAN Neural Network Modeling Based On Rough Set Theory
177(11)
6.5.1 OIF-Elman Neural Network Model
179(2)
6.5.2 OIF-Elman Neural Network with Simplified PSO
181(1)
6.5.3 Correction on Predicted Values of Power Amplifier Based on Rough Set Theory
181(3)
6.5.4 Power Amplifier Modeling Simulation and Results
184(4)
6.6 Neural Network Inverse Modeling Method And Applications
188(12)
6.6.1 Inverse Modeling Method
191(1)
6.6.2 Update Algorithm
192(3)
6.6.3 Application Examples and Simulation Analysis
195(5)
References
200(5)
Chapter 7 Power Amplifier Modeling with X-Parameters 205(20)
7.1 Design Of Wideband Power Amplifier Based On X-Parameter Transistor Model
205(9)
7.1.1 Extraction of X-Parameters
207(1)
7.1.2 X-Parameter Model Description
208(1)
7.1.3 Load-Independent X-Parameter Extraction Method
208(3)
7.1.4 Wideband Power Amplifier Design
211(1)
7.1.5 Simulation and Testing
212(2)
7.2 Research On Dynamic X-Parameter Model Based On Memory Effects Of Power Amplifier
214(8)
7.2.1 Dynamic X-Parameter Theory
215(3)
7.2.2 Improved Dynamic X-Parameter Model
218(2)
7.2.3 Kernel Function Extraction of New Model
220(1)
7.2.4 Simulation and Data Analysis
221(1)
References
222(3)
Chapter 8 Other Power Amplifier Modeling 225(18)
8.1 Power Amplifier Model Based On Dynamic Rational Function And Predistortion Applications
225(10)
8.1.1 Model Analysis
226(3)
8.1.2 Model Determination and Coefficient Extraction
229(1)
8.1.3 Model Performance Evaluation
230(3)
8.1.4 Predistortion Application
233(2)
8.2 RF Power Amplifier Model Based On Particle Swarm Optimization (PSO) Support Vector Machine (SVM)
235(5)
8.2.1 SVM and PSO
236(1)
8.2.2 Simulation Experiment and Result Analysis
237(3)
References
240(3)
Chapter 9 Nonlinear Circuit Analysis Methods 243(34)
9.1 Application Of Hybrid Genetic Algorithm With Volterra Series-Based Improvement In Harmonic Balance
243(14)
9.1.1 Harmonic Balance Theory
244(3)
9.1.2 Improved Hybrid Genetic Algorithm
247(6)
9.1.3 Simulation and Data Analysis
253(4)
9.2 Application Of Quasi-Newtonian Particle Swarm Optimization Algorithm In Harmonic Balance Equations For Nonlinear Circuits
257(9)
9.2.1 Harmonic Balance Theory
258(3)
9.2.2 Quasi-Newtonian PSO Algorithm
261(2)
9.2.3 Experimental Simulation Analysis
263(3)
9.3 Application Of Hybrid Ant Colony Algorithm In Nonlinear Harmonic Balance Analysis
266(8)
9.3.1 Fundamentals of Harmonic Balance
267(1)
9.3.2 Hybrid Ant Colony Algorithm
268(3)
9.3.3 Experimental Simulation Analysis
271(3)
References
274(3)
Chapter 10 Predistortion Algorithms and Applications 277
10.1 Theoretical Analysis And Simulation Implementation Of Digital Baseband Predistortion For Power Amplifier
278(8)
10.1.1 Digital Baseband Predistortion Structure
279(2)
10.1.2 Theoretical Derivation of Transfer Function for Digital Predistorter
281(1)
10.1.3 Simulation Implementation of Digital Baseband Predistortion
282(4)
10.2 Research On Digital Predistortion Method Of Double-Loop Structure
286(6)
10.2.1 Predistortion Structure of Double-Loop Structure
286(3)
10.2.2 Experimental Validation and Result Analysis
289(3)
10.3 Application Of Peak-To-Average Ratio Suppression And Predistortion In OFDM-ROF System
292(10)
10.3.1 OFDM-ROF System Analysis
292(3)
10.3.2 Nonlinear Distortion Analysis of OFDM-ROF System
295(1)
10.3.3 Co-Simulation System Establishment
296(2)
10.3.4 Co-Simulation Result
298(4)
10.4 Combined Scheme Of Peak-To-Average Ratio Suppression And Predistortion Technology With Improved Algorithm
302(13)
10.4.1 System Model
303(1)
10.4.2 Digital Predistortion System
304(3)
10.4.3 Combined Scheme of Predistortion and Peak- to-Average Ratio Suppression
307(3)
10.4.4 Experimental Result and Analysis
310(5)
10.5 Sparse Normalized Power Amplifier Model And Predistortion Application
315(10)
10.5.1 Model Description
316(2)
10.5.2 Model Sparsification and Identification
318(3)
10.5.3 Model Performance Validation
321(1)
10.5.4 Predistortion Application
322(3)
10.6 Combined Predistortion Method Of Simplified Filter Look-Up Table And Neural Network
325(10)
10.6.1 Filter Look-Up Table Predistortion
326(1)
10.6.2 Predistortion Scheme Combining Improved Filter Look-Up Table and Neural Network
327(4)
10.6.3 Experimental Result and Analysis
331(4)
10.7 Adaptive Predistortion Method With Offline Training Based On BP Inverse Model
335(11)
10.7.1 Adaptive Predistortion Method with Offline Training Based on BP Neural Network
337(5)
10.7.2 Experiment and Comparative Analysis
342(4)
10.8 Power Amplifier Predistortion Method Based On Adaptive Fuzzy Neural Network
346(11)
10.8.1 Fuzzy Neural Network Model Structure
347(2)
10.8.2 New Method for Adaptive Predistortion
349(4)
10.8.3 Experimental Validation Analysis
353(4)
References
357
Jingchang Nan is Professor and doctoral supervisor at Liaoning Technical University. He is also a senior visiting scholar at the University of Michigan. His research interests include RF circuit and systems, communication signal processing and electromagnetic and information processing.

Mingming Gao is Associate Professor and the Vice President of the School of Electronic and Information Engineering, Liaoning Technical University. Her research interests focus on communication engineering, intelligent RF technology, RF circuits and systems, electromagnetic compatibility, linearization technology of power amplifiers and artificial intelligence among other topics.
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