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Behavioral Modeling and Predistortion of Wideband Wireless Transmitters

Book Description

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.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright
  4. About the Authors
  5. Preface
  6. Acknowledgments
  7. Chapter 1: Characterization of Wireless Transmitter Distortions
    1. 1.1 Introduction
    2. 1.2 Impact of Distortions on Transmitter Performances
    3. 1.3 Output Power versus Input Power Characteristic
    4. 1.4 AM/AM and AM/PM Characteristics
    5. 1.5 1 dB Compression Point
    6. 1.6 Third and Fifth Order Intercept Points
    7. 1.7 Carrier to Inter-Modulation Distortion Ratio
    8. 1.8 Adjacent Channel Leakage Ratio
    9. 1.9 Error Vector Magnitude
    10. References
  8. Chapter 2: Dynamic Nonlinear Systems
    1. 2.1 Classification of Nonlinear Systems
    2. 2.2 Memory in Microwave Power Amplification Systems
    3. 2.3 Baseband and Low-Pass Equivalent Signals
    4. 2.4 Origins and Types of Memory Effects in Power Amplification Systems
    5. 2.5 Volterra Series Models
    6. References
  9. Chapter 3: Model Performance Evaluation
    1. 3.1 Introduction
    2. 3.2 Behavioral Modeling versus Digital Predistortion
    3. 3.3 Time Domain Metrics
    4. 3.4 Frequency Domain Metrics
    5. 3.5 Static Nonlinearity Cancelation Techniques
    6. 3.6 Discussion and Conclusion
    7. References
  10. Chapter 4: Quasi-Memoryless Behavioral Models
    1. 4.1 Introduction
    2. 4.2 Modeling and Simulation of Memoryless/Quasi-Memoryless Nonlinear Systems
    3. 4.3 Bandpass to Baseband Equivalent Transformation
    4. 4.4 Look-Up Table Models
    5. 4.5 Generic Nonlinear Amplifier Behavioral Model
    6. 4.6 Empirical Analytical Based Models
    7. 4.7 Power Series Models
    8. References
  11. Chapter 5: Memory Polynomial Based Models
    1. 5.1 Introduction
    2. 5.2 Generic Memory Polynomial Model Formulation
    3. 5.3 Memory Polynomial Model
    4. 5.4 Variants of the Memory Polynomial Model
    5. 5.5 Envelope Memory Polynomial Model
    6. 5.6 Generalized Memory Polynomial Model
    7. 5.7 Hybrid Memory Polynomial Model
    8. 5.8 Dynamic Deviation Reduction Volterra Model
    9. 5.9 Comparison and Discussion
    10. References
  12. Chapter 6: Box-Oriented Models
    1. 6.1 Introduction
    2. 6.2 Hammerstein and Wiener Models
    3. 6.3 Augmented Hammerstein and Weiner Models
    4. 6.4 Three-Box Wiener–Hammerstein Models
    5. 6.5 Two-Box Polynomial Models
    6. 6.6 Three-Box Polynomial Models
    7. 6.7 Polynomial Based Model with I/Q and DC Impairments
    8. References
  13. Chapter 7: Neural Network Based Models
    1. 7.1 Introduction
    2. 7.2 Basics of Neural Networks
    3. 7.3 Neural Networks Architecture for Modeling of Complex Static Systems
    4. 7.4 Neural Networks Architecture for Modeling of Complex Dynamic Systems
    5. 7.5 Training Algorithms
    6. 7.6 Conclusion
    7. References
  14. Chapter 8: Characterization and Identification Techniques
    1. 8.1 Introduction
    2. 8.2 Test Signals for Power Amplifier and Transmitter Characterization
    3. 8.3 Data De-Embedding in Modulated Signal Based Characterization
    4. 8.4 Identification Techniques
    5. 8.5 Robustness of System Identification Algorithms
    6. 8.6 Conclusions
    7. References
  15. Chapter 9: Baseband Digital Predistortion
    1. 9.1 The Predistortion Concept
    2. 9.2 Adaptive Digital Predistortion
    3. 9.3 The Predistorter's Power Range in Indirect Learning Architectures
    4. 9.4 Small Signal Gain Normalization
    5. 9.5 Digital Predistortion Implementations
    6. 9.6 The Bandwidth and Power Scalable Digital Predistortion Technique
    7. 9.7 Summary
    8. References
  16. Chapter 10: Advanced Modeling and Digital Predistortion
    1. 10.1 Joint Quadrature Impairment and Nonlinear Distortion Compensation Using Multi-Input DPD
    2. 10.2 Modeling and Linearization of Nonlinear MIMO Systems
    3. 10.3 Modeling and Linearization of Dual-Band Transmitters
    4. 10.4 Application of MIMO and Dual-Band Models in Digital Predistortion
    5. References
  17. Index
  18. End User License Agreement