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E-grāmata: Behavioral Modeling and Predistortion of Wideband Wireless Transmitters [Wiley Online]

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  • Formāts: 272 pages
  • Izdošanas datums: 24-Jul-2015
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 111900442X
  • ISBN-13: 9781119004424
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  • Wiley Online
  • Cena: 122,22 €*
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Behavioral Modeling and Predistortion of Wideband Wireless TransmittersPalielināt
  • Formāts: 272 pages
  • Izdošanas datums: 24-Jul-2015
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 111900442X
  • ISBN-13: 9781119004424
Citas grāmatas par šo tēmu:
Wiley Online E-booksMore info about Wiley Online
Rokasgrāmatas mājas lapa: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119004424
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.
About the Authors xi
Preface xiii
Acknowledgments xv
1 Characterization of Wireless Transmitter Distortions 1(22)
1.1 Introduction
1(5)
1.1.1 RF Power Amplifier Nonlinearity
2(1)
1.1.2 Inter-Modulation Distortion and Spectrum Regrowth
2(4)
1.2 Impact of Distortions on Transmitter Performances
6(3)
1.3 Output Power versus Input Power Characteristic
9(1)
1.4 AM/AM and AM/PM Characteristics
10(2)
1.5 1 dB Compression Point
12(3)
1.6 Third and Fifth Order Intercept Points
15(1)
1.7 Carrier to Inter-Modulation Distortion Ratio
16(2)
1.8 Adjacent Channel Leakage Ratio
18(1)
1.9 Error Vector Magnitude
19(2)
References
21(2)
2 Dynamic Nonlinear Systems 23(20)
2.1 Classification of Nonlinear Systems
23(2)
2.1.1 Memoryless Systems
23(1)
2.1.2 Systems with Memory
24(1)
2.2 Memory in Microwave Power Amplification Systems
25(2)
2.2.1 Nonlinear Systems without Memory
25(1)
2.2.2 Weakly Nonlinear and Quasi-Memoryless Systems
26(1)
2.2.3 Nonlinear System with Memory
27(1)
2.3 Baseband and Low-Pass Equivalent Signals
27(2)
2.4 Origins and Types of Memory Effects in Power Amplification Systems
29(9)
2.4.1 Origins of Memory Effects
29(1)
2.4.2 Electrical Memory Effects
30(3)
2.4.3 Thermal Memory Effects
33(5)
2.5 Volterra Series Models
38(2)
References
40(3)
3 Model Performance Evaluation 43(20)
3.1 Introduction
43(1)
3.2 Behavioral Modeling versus Digital Predistortion
43(3)
3.3 Time Domain Metrics
46(2)
3.3.1 Normalized Mean Square Error
46(1)
3.3.2 Memory Effects Modeling Ratio
47(1)
3.4 Frequency Domain Metrics
48(4)
3.4.1 Frequency Domain Normalized Mean Square Error
48(1)
3.4.2 Adjacent Channel Error Power Ratio
49(1)
3.4.3 Weighted Error Spectrum Power Ratio
50(1)
3.4.4 Normalized Absolute Mean Spectrum Error
51(1)
3.5 Static Nonlinearity Cancelation Techniques
52(9)
3.5.1 Static Nonlinearity Pre-Compensation Technique
52(4)
3.5.2 Static Nonlinearity Post-Compensation Technique
56(3)
3.5.3 Memory Effect Intensity
59(2)
3.6 Discussion and Conclusion
61(1)
References
62(1)
4 Quasi-Memoryless Behavioral Models 63(26)
4.1 Introduction
63(1)
4.2 Modeling and Simulation of Memoryless/Quasi-Memoryless Nonlinear Systems
63(4)
4.3 Bandpass to Baseband Equivalent Transformation
67(2)
4.4 Look-Up Table Models
69(2)
4.4.1 Uniformly Indexed Loop-Up Tables
69(1)
4.4.2 Non-Uniformly Indexed Look-Up Tables
70(1)
4.5 Generic Nonlinear Amplifier Behavioral Model
71(2)
4.6 Empirical Analytical Based Models
73(9)
4.6.1 Polar Saleh Model
73(1)
4.6.2 Cartesian Saleh Model
74(2)
4.6.3 Frequency-Dependent Saleh Model
76(1)
4.6.4 Ghorbani Model
76(1)
4.6.5 Berman and Mahle Phase Model
77(1)
4.6.6 Thomas—Weidner—Durrani Amplitude Model
77(1)
4.6.7 Limiter Model
78(1)
4.6.8 ARCTAN Model
79(2)
4.6.9 Rapp Model
81(1)
4.6.10 White Model
82(1)
4.7 Power Series Models
82(4)
4.7.1 Polynomial Model
82(1)
4.7.2 Bessel Function Based Model
83(1)
4.7.3 Chebyshev Series Based Model
84(1)
4.7.4 Gegenbauer Polynomials Based Model
84(1)
4.7.5 Zernike Polynomials Based Model
85(1)
References
86(3)
5 Memory Polynomial Based Models 89(26)
5.1 Introduction
89(1)
5.2 Generic Memory Polynomial Model Formulation
90(1)
5.3 Memory Polynomial Model
91(1)
5.4 Variants of the Memory Polynomial Model
91(7)
5.4.1 Orthogonal Memory Polynomial Model
91(2)
5.4.2 Sparse-Delay Memory Polynomial Model
93(2)
5.4.3 Exponentially Shaped Memory Delay Profile Memory Polynomial Model
95(1)
5.4.4 Non-Uniform Memory Polynomial Model
96(1)
5.4.5 Unstructured Memory Polynomial Model
97(1)
5.5 Envelope Memory Polynomial Model
98(3)
5.6 Generalized Memory Polynomial Model
101(5)
5.7 Hybrid Memory Polynomial Model
106(2)
5.8 Dynamic Deviation Reduction Volterra Model
108(3)
5.9 Comparison and Discussion
111(2)
References
113(2)
6 Box-Oriented Models 115(18)
6.1 Introduction
115(1)
6.2 Hammerstein and Wiener Models
115(3)
6.2.1 Wiener Model
116(1)
6.2.2 Hammerstein Model
117(1)
6.3 Augmented Hammerstein and Weiner Models
118(2)
6.3.1 Augmented Wiener Model
118(1)
6.3.2 Augmented Hammerstein Model
119(1)
6.4 Three-Box Wiener—Hammerstein Models
120(3)
6.4.1 Wiener—Hammerstein Model
120(1)
6.4.2 Hammerstein—Wiener Model
120(1)
6.4.3 Feedforward Hammerstein Model
121(2)
6.5 Two-Box Polynomial Models
123(1)
6.5.1 Models' Descriptions
123(1)
6.5.2 Identification Procedure
124(1)
6.6 Three-Box Polynomial Models
124(4)
6.6.1 Parallel Three-Blocks Model: PLUME Model
124(1)
6.6.2 Three Layered Biased Memory Polynomial Model
125(2)
6.6.3 Rational Function Model for Amplifiers
127(1)
6.7 Polynomial Based Model with I/Q and DC Impairments
128(2)
6.7.1 Parallel Hammerstein (PH) Based Model for the Alleviation of Various Imperfections in Direct Conversion Transmitters
129(1)
6.7.2 Two-Box Model with 1/Q and DC Impairments
129(1)
References
130(3)
7 Neural Network Based Models 133(20)
7.1 Introduction
133(1)
7.2 Basics of Neural Networks
133(4)
7.3 Neural Networks Architecture for Modeling of Complex Static Systems
137(3)
7.3.1 Single-Input Single-Output Feedforward Neural Network (SISO-FFNN)
137(1)
7.3.2 Dual-Input Dual-Output Feedforward Neural Network (DIDO-FFNN)
138(1)
7.3.3 Dual-Input Dual-Output Coupled Cartesian Based Neural Network (DIDO-CC-NN)
139(1)
7.4 Neural Networks Architecture for Modeling of Complex Dynamic Systems
140(7)
7.4.1 Complex Time-Delay Recurrent Neural Network (CTDRNN)
141(1)
7.4.2 Complex Time-Delay Neural Network (CTDNN)
142(1)
7.4.3 Real Valued Time-Delay Recurrent Neural Network (RVTDRNN)
142(2)
7.4.4 Real Valued Time-Delay Neural Network (RVTDNN)
144(3)
7.5 Training Algorithms
147(3)
7.6 Conclusion
150(1)
References
151(2)
8 Characterization and Identification Techniques 153(32)
8.1 Introduction
153(2)
8.2 Test Signals for Power Amplifier and Transmitter Characterization
155(8)
8.2.1 Characterization Using Continuous Wave Signals
155(1)
8.2.2 Characterization Using Two-Tone Signals
156(1)
8.2.3 Characterization Using Multi-Tone Signals
157(1)
8.2.4 Characterization Using Modulated Signals
158(2)
8.2.5 Characterization Using Synthetic Modulated Signals
160(1)
8.2.6 Discussion: Impact of Test Signal on the Measured AM/AM and AM/PM Characteristics
160(3)
8.3 Data De-Embedding in Modulated Signal Based Characterization
163(7)
8.4 Identification Techniques
170(9)
8.4.1 Moving Average Techniques
170(2)
8.4.2 Model Coefficient Extraction Techniques
172(7)
8.5 Robustness of System Identification Algorithms
179(2)
8.5.1 The LS Algorithm
179(1)
8.5.2 The LMS Algorithm
179(1)
8.5.3 The RLS Algorithm
180(1)
8.6 Conclusions
181(1)
References
181(4)
9 Baseband Digital Predistortion 185(24)
9.1 The Predistortion Concept
185(3)
9.2 Adaptive Digital Predistortion
188(3)
9.2.1 Closed Loop Adaptive Digital Predistorters
188(1)
9.2.2 Open Loop Adaptive Digital Predistorters
189(2)
9.3 The Predistorter's Power Range in Indirect Learning Architectures
191(3)
9.3.1 Constant Peak Power Technique
193(1)
9.3.2 Constant Average Power Technique
193(1)
9.3.3 Synergetic CFR and DPD Technique
194(1)
9.4 Small Signal Gain Normalization
194(7)
9.5 Digital Predistortion Implementations
201(4)
9.5.1 Baseband Digital Predistortion
201(3)
9.5.2 RF Digital Predistortion
204(1)
9.6 The Bandwidth and Power Scalable Digital Predistortion Technique
205(1)
9.7 Summary
206(1)
References
207(2)
10 Advanced Modeling and Digital Predistortion 209(38)
10.1 Joint Quadrature Impairment and Nonlinear Distortion Compensation Using Multi-Input DPD
209(7)
10.1.1 Modeling of Quadrature Modulator Imperfections
210(1)
10.1.2 Dual-Input Polynomial Model for Memoryless Joint Modeling of Quadrature Imbalance and PA Distortions
211(1)
10.1.3 Dual-Input Memory Polynomial for Joint Modeling of Quadrature Imbalance and PA Distortions Including Memory Effects
212(1)
10.1.4 Dual-Branch Parallel Hammerstein Model for Joint Modeling of Quadrature Imbalance and PA Distortions with Memory
213(3)
10.1.5 Dual-Conjugate-Input Memory Polynomial for Joint Modeling of Quadrature Imbalance and PA Distortions Including Memory Effects
216(1)
10.2 Modeling and Linearization of Nonlinear MIMO Systems
216(11)
10.2.1 Impairments in MIMO Systems
216(5)
10.2.2 Crossover Polynomial Model for MIMO Transmitters
221(1)
10.2.3 Dual-Input Nonlinear Polynomial Model for MIMO Transmitters
222(1)
10.2.4 MIMO Transmitters Nonlinear Multi-Variable Polynomial Model
223(4)
10.3 Modeling and Linearization of Dual-Band Transmitters
227(8)
10.3.1 Generalization of the Polynomial Model to the Dual-Band Case
228(2)
10.3.2 Two-Dimensional (2-D) Memory Polynomial Model for Dual-Band Transmitters
230(1)
10.3.3 Phase-Aligned Multi-band Volterra DPD
231(4)
10.4 Application of MIMO and Dual-Band Models in Digital Predistortion
235(7)
10.4.1 Linearization of MIMO Systems with Nonlinear Crosstalk
236(2)
10.4.2 Linearization of Concurrent Dual-Band Transmitters Using a 2-D Memory Polynomial Model
238(2)
10.4.3 Linearization of Concurrent Tri-Band Transmitters Using 3-D Phase-Aligned Volterra Model
240(2)
References
242(5)
Index 247
Fadhel M. Ghannouchi University of Calgary, Canada

Oualid Hammi King Fahd University of Petroleum and Minerals, Saudi Arabia

Mohamed Helaoui University of Calgary, Canada
Permanent link: https://www.kriso.lv/db/9781119004424_pe.html
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