Behavioral Modeling and Predistortion of Wideband Wireless Transmitters.

Intro -- Title Page -- Copyright -- Table of Contents -- About the Authors -- Preface -- Acknowledgments -- Chapter 1: Characterization of Wireless Transmitter Distortions -- 1.1 Introduction -- 1.2 Impact of Distortions on Transmitter Performances -- 1.3 Output Power versus Input Power Characteristic -- 1.4 AM/AM and AM/PM Characteristics -- 1.5 1 dB Compression Point -- 1.6 Third and Fifth Order Intercept Points -- 1.7 Carrier to Inter-Modulation Distortion Ratio -- 1.8 Adjacent Channel Leakage Ratio -- 1.9 Error Vector Magnitude -- References -- Chapter 2: Dynamic Nonlinear Systems -- 2.1 Classification of Nonlinear Systems -- 2.2 Memory in Microwave Power Amplification Systems -- 2.3 Baseband and Low-Pass Equivalent Signals -- 2.4 Origins and Types of Memory Effects in Power Amplification Systems -- 2.5 Volterra Series Models -- References -- Chapter 3: Model Performance Evaluation -- 3.1 Introduction -- 3.2 Behavioral Modeling versus Digital Predistortion -- 3.3 Time Domain Metrics -- 3.4 Frequency Domain Metrics -- 3.5 Static Nonlinearity Cancelation Techniques -- 3.6 Discussion and Conclusion -- References -- Chapter 4: Quasi-Memoryless Behavioral Models -- 4.1 Introduction -- 4.2 Modeling and Simulation of Memoryless/Quasi-Memoryless Nonlinear Systems -- 4.3 Bandpass to Baseband Equivalent Transformation -- 4.4 Look-Up Table Models -- 4.5 Generic Nonlinear Amplifier Behavioral Model -- 4.6 Empirical Analytical Based Models -- 4.7 Power Series Models -- References -- Chapter 5: Memory Polynomial Based Models -- 5.1 Introduction -- 5.2 Generic Memory Polynomial Model Formulation -- 5.3 Memory Polynomial Model -- 5.4 Variants of the Memory Polynomial Model -- 5.5 Envelope Memory Polynomial Model -- 5.6 Generalized Memory Polynomial Model -- 5.7 Hybrid Memory Polynomial Model -- 5.8 Dynamic Deviation Reduction Volterra Model.

5.9 Comparison and Discussion -- References -- Chapter 6: Box-Oriented Models -- 6.1 Introduction -- 6.2 Hammerstein and Wiener Models -- 6.3 Augmented Hammerstein and Weiner Models -- 6.4 Three-Box Wiener-Hammerstein Models -- 6.5 Two-Box Polynomial Models -- 6.6 Three-Box Polynomial Models -- 6.7 Polynomial Based Model with I/Q and DC Impairments -- References -- Chapter 7: Neural Network Based Models -- 7.1 Introduction -- 7.2 Basics of Neural Networks -- 7.3 Neural Networks Architecture for Modeling of Complex Static Systems -- 7.4 Neural Networks Architecture for Modeling of Complex Dynamic Systems -- 7.5 Training Algorithms -- 7.6 Conclusion -- References -- Chapter 8: Characterization and Identification Techniques -- 8.1 Introduction -- 8.2 Test Signals for Power Amplifier and Transmitter Characterization -- 8.3 Data De-Embedding in Modulated Signal Based Characterization -- 8.4 Identification Techniques -- 8.5 Robustness of System Identification Algorithms -- 8.6 Conclusions -- References -- Chapter 9: Baseband Digital Predistortion -- 9.1 The Predistortion Concept -- 9.2 Adaptive Digital Predistortion -- 9.3 The Predistorter's Power Range in Indirect Learning Architectures -- 9.4 Small Signal Gain Normalization -- 9.5 Digital Predistortion Implementations -- 9.6 The Bandwidth and Power Scalable Digital Predistortion Technique -- 9.7 Summary -- References -- Chapter 10: Advanced Modeling and Digital Predistortion -- 10.1 Joint Quadrature Impairment and Nonlinear Distortion Compensation Using Multi-Input DPD -- 10.2 Modeling and Linearization of Nonlinear MIMO Systems -- 10.3 Modeling and Linearization of Dual-Band Transmitters -- 10.4 Application of MIMO and Dual-Band Models in Digital Predistortion -- References -- Index -- End User License Agreement.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.