book

Behavioral Modeling and Predistortion of Wideband Wireless Transmitters

by Fadhel M. Ghannouchi, Oualid Hammi, Mohamed Helaoui

July 2015

Intermediate to advanced

272 pages

8h 14m

English

Wiley

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1.1 Introduction1.2 Impact of Distortions on Transmitter Performances1.3 Output Power versus Input Power Characteristic1.4 AM/AM and AM/PM Characteristics1.5 1 dB Compression Point1.6 Third and Fifth Order Intercept Points1.7 Carrier to Inter-Modulation Distortion Ratio1.8 Adjacent Channel Leakage Ratio1.9 Error Vector MagnitudeReferences
2.1 Classification of Nonlinear Systems2.2 Memory in Microwave Power Amplification Systems2.3 Baseband and Low-Pass Equivalent Signals2.4 Origins and Types of Memory Effects in Power Amplification Systems2.5 Volterra Series ModelsReferences
3.1 Introduction3.2 Behavioral Modeling versus Digital Predistortion3.3 Time Domain Metrics3.4 Frequency Domain Metrics3.5 Static Nonlinearity Cancelation Techniques3.6 Discussion and ConclusionReferences
4.1 Introduction4.2 Modeling and Simulation of Memoryless/Quasi-Memoryless Nonlinear Systems4.3 Bandpass to Baseband Equivalent Transformation4.4 Look-Up Table Models4.5 Generic Nonlinear Amplifier Behavioral Model4.6 Empirical Analytical Based Models4.7 Power Series ModelsReferences

5.1 Introduction5.2 Generic Memory Polynomial Model Formulation5.3 Memory Polynomial Model5.4 Variants of the Memory Polynomial Model5.5 Envelope Memory Polynomial Model5.6 Generalized Memory Polynomial Model5.7 Hybrid Memory Polynomial Model5.8 Dynamic Deviation Reduction Volterra Model5.9 Comparison and DiscussionReferences
6.1 Introduction6.2 Hammerstein and Wiener Models6.3 Augmented Hammerstein and Weiner Models6.4 Three-Box Wiener–Hammerstein Models6.5 Two-Box Polynomial Models6.6 Three-Box Polynomial Models6.7 Polynomial Based Model with I/Q and DC ImpairmentsReferences
7.1 Introduction7.2 Basics of Neural Networks7.3 Neural Networks Architecture for Modeling of Complex Static Systems7.4 Neural Networks Architecture for Modeling of Complex Dynamic Systems7.5 Training Algorithms7.6 ConclusionReferences
8.1 Introduction8.2 Test Signals for Power Amplifier and Transmitter Characterization8.3 Data De-Embedding in Modulated Signal Based Characterization8.4 Identification Techniques8.5 Robustness of System Identification Algorithms8.6 ConclusionsReferences
9.1 The Predistortion Concept9.2 Adaptive Digital Predistortion9.3 The Predistorter's Power Range in Indirect Learning Architectures9.4 Small Signal Gain Normalization9.5 Digital Predistortion Implementations9.6 The Bandwidth and Power Scalable Digital Predistortion Technique9.7 SummaryReferences
10.1 Joint Quadrature Impairment and Nonlinear Distortion Compensation Using Multi-Input DPD10.2 Modeling and Linearization of Nonlinear MIMO Systems10.3 Modeling and Linearization of Dual-Band Transmitters10.4 Application of MIMO and Dual-Band Models in Digital PredistortionReferences

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