Before the emergence of ultra-wideband (UWB) radios, widely used wireless communications were based on sinusoidal carriers, and impulse technologies were employed only in specific applications (e.g. radar). In 2002, the Federal Communication Commission (FCC) allowed unlicensed operation between 3.1–10.6 GHz for UWB communication, using a wideband signal format with a low EIRP level (−41.3dBm/MHz). UWB communication systems then emerged as an alternative to narrowband systems and significant effort in this area has been invested at the regulatory, commercial, and research levels.

IEEE Transactions on Microwave Theory and Techniques and Antennas and Propagation Announce a Joint Special Issue on Ultra-Wideband (UWB) Technology

This paper presents a comparative overview of the most important approaches presented to address the behavioral modeling of microwave and wireless power amplifiers (PAs). Starting from a theoretical framework of recursive and nonrecursive nonlinear filters, it proposes a classification of the various PA behavioral models, discussing their abilities to represent the different effects observed in practical circuits. Using that formal procedure, one explains how it was possible to integrate a wide range of behavioral modeling activities and to show that some of them, which at first glance seemed to be quite different, are, indeed, identical in their modeling capabilities.

A comparative overview of microwave and wireless power-amplifier behavioral modeling approaches

A new representation of the Volterra series is proposed, which is derived from a previously introduced modified Volterra series, but adapted to the discrete time domain and reformulated in a novel way. Based on this representation, an efficient model-pruning approach, called dynamic deviation reduction, is introduced to simplify the structure of Volterra-series-based RF power amplifier behavioral models aimed at significantly reducing the complexity of the model, but without incurring loss of model fidelity. Both static nonlinearities and different orders of dynamic behavior can be separately identified and the proposed representation retains the important property of linearity with respect to series coefficients. This model can, therefore, be easily extracted directly from the measured time domain of input and output samples of an amplifier by employing simple linear system identification algorithms. A systematic mathematical derivation is presented, together with validation of the proposed method using both computer simulation and experiment

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Dynamic Deviation Reduction-Based Volterra Behavioral Modeling of RF Power Amplifiers

Preface. About the Authors. Acknowledgments. 1 Power Amplifier Fundamentals. 1.1 Introduction. 1.2 Definition of Power Amplifier Parameters. 1.3 Distortion Parameters. 1.4 Power Match Condition. 1.5 Class of Operation. 1.6 Overview of Semiconductors for PAs. 1.7 Devices for PA. 1.8 Appendix: Demonstration of Useful Relationships. 1.9 References. 2 Power Amplifier Design. 2.1 Introduction. 2.2 Design Flow. 2.3 Simplified Approaches. 2.4 The Tuned Load Amplifier. 2.5 Sample Design of a Tuned Load PA. 2.6 References. 3 Nonlinear Analysis for Power Amplifiers. 3.1 Introduction. 3.2 Linear vs. Nonlinear Circuits. 3.3 Time Domain Integration. 3.4 Example. 3.5 Solution by Series Expansion. 3.6 The Volterra Series. 3.7 The Fourier Series. 3.8 The Harmonic Balance. 3.9 Envelope Analysis. 3.10 Spectral Balance. 3.11 Large Signal Stability Issue. 3.12 References. 4 Load Pull. 4.1 Introduction. 4.2 Passive Source/Load Pull Measurement Systems. 4.3 Active Source/Load Pull Measurement Systems. 4.4 Measurement Test-sets. 4.5 Advanced Load Pull Measurements. 4.6 Source/Load Pull Characterization. 4.7 Determination of Optimum Load Condition. 4.8 Appendix: Construction of Simplified Load Pull Contours through Linear Simulations. 4.9 References. 5 High Efficiency PA Design Theory. 5.1 Introduction. 5.2 Power Balance in a PA. 5.3 Ideal Approaches. 5.4 High Frequency Harmonic Tuning Approaches. 5.5 High Frequency Third Harmonic Tuned (Class F). 5.6 High Frequency Second Harmonic Tuned. 5.7 High Frequency Second and Third Harmonic Tuned. 5.8 Design by Harmonic Tuning. 5.9 Final Remarks. 5.10 References. 6 Switched Amplifiers. 6.1 Introduction. 6.2 The Ideal Class E Amplifier. 6.3 Class E Behavioural Analysis. 6.4 Low Frequency Class E Amplifier Design. 6.5 Class E Amplifier Design with 50# Duty-cycle. 6.6 Examples of High Frequency Class E Amplifiers. 6.7 Class E vs. Harmonic Tuned. 6.8 Class E Final Remarks. 6.9 Appendix: Demonstration of Useful Relationships. 6.10 References. 7 High Frequency Class F Power Amplifiers. 7.1 Introduction. 7.2 Class F Description Based on Voltage Wave-shaping. 7.3 High Frequency Class F Amplifiers. 7.4 Bias Level Selection. 7.5 Class F Output Matching Network Design. 7.6 Class F Design Examples. 7.7 References. 8 High Frequency Harmonic Tuned Power Amplifiers. 8.1 Introduction. 8.2 Theory of Harmonic Tuned PA Design. 8.3 Input Device Nonlinear Phenomena: Theoretical Analysis. 8.4 Input Device Nonlinear Phenomena: Experimental Results. 8.5 Output Device Nonlinear Phenomena. 8.6 Design of a Second HT Power Amplifier. 8.7 Design of a Second and Third HT Power Amplifier. 8.8 Example of 2nd HT GaN PA. 8.9 Final Remarks. 8.10 References. 9 High Linearity in Efficient Power Amplifiers. 9.1 Introduction. 9.2 Systems Classification. 9.3 Linearity Issue. 9.4 Bias Point Influence on IMD. 9.5 Harmonic Loading Effects on IMD. 9.6 Appendix: Volterra Analysis Example. 9.7 References. 10 Power Combining. 10.1 Introduction. 10.2 Device Scaling Properties. 10.3 Power Budget. 10.4 Power Combiner Classification. 10.5 The T-junction Power Divider. 10.6 Wilkinson Combiner. 10.7 The Quadrature (90 ) Hybrid. 10.8 The 180 Hybrid (Ring Coupler or Rat-race). 10.9 Bus-bar Combiner. 10.10 Other Planar Combiners. 10.11 Corporate Combiners. 10.12 Resonating Planar Combiners. 10.13 Graceful Degradation. 10.14 Matching Properties of Combined PAs. 10.15 Unbalance Issue in Hybrid Combiners. 10.16 Appendix: Basic Properties of Three-port Networks. 10.17 References. 11 The Doherty Power Amplifier. 11.1 Introduction. 11.2 Doherty's Idea. 11.3 The Classical Doherty Configuration. 11.4 The 'AB-C' Doherty Amplifier Analysis. 11.5 Power Splitter Sizing. 11.6 Evaluation of the Gain in a Doherty Amplifier. 11.7 Design Example. 11.8 Advanced Solutions. 11.9 References. Index.

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High Efficiency RF and Microwave Solid State Power Amplifiers

A comparative study of state-of-the-art behavioral models for microwave power amplifiers (PAs) is presented in this paper. After establishing a proper definition for accuracy and complexity for PA behavioral models, a short description on various behavioral models is presented. The main focus of this paper is on the modeling accuracy as a function of computational complexity. Data is collected from measurements on two PAs-a general-purpose amplifier and a Doherty PA designed for WiMAX-for different output power levels. The models are characterized in terms of accuracy and complexity for both in-band and out-of-band error. The results show that, among the models studied, the generalized memory polynomial behavioral model has the best tradeoff for accuracy versus complexity for both PAs, and can obtain high performance at half of the computational cost of all other models analyzed.

https://publications.lib.chalmers.se/records/fulltext/local_121905.pdf

A Comparative Analysis of the Complexity/Accuracy Tradeoff in Power Amplifier Behavioral Models

In this paper an artificial neural network approach for nonlinear modelling of a 10-W LDMOSFET is presented. The model extraction is based on DC and scattering parameter measurements. In particular, artificial neural networks are used to model the dependence of both DC drain current and intrinsic capacitances with respect to the intrinsic gate and drain voltages. The model validation is successfully achieved by comparing the simulation results with time-domain nonlinear measurements.

A neural network approach for nonlinear modelling of LDMOSFETs

In this manuscript reliability issues in GaAs pHEMTs are empirically investigated. Starting from typical reliability approaches where the average value of the gate current is dealt with, we show that an exhaustive information for reliability analysis can be obtained only by looking at the gate current dynamic behaviour. Different experimental examples are provided in order to demonstrate the validity of the proposed considerations.

http://ieeexplore.ieee.org/iel7/6476772/6483692/06483785.pdf

Influence of the gate current dynamic behaviour on GaAs HEMT reliability issues

Many different nonlinear modeling approaches for electron devices have been proposed in the last few years, and quite often circuit designers suffer from the lack of reliable comparison criteria, on the basis of which the most suitable model for a specific application can be identified. Moreover, similar strategies are needed even by research groups, whose activity is devoted to the model identification and extraction, in order to quantify the degree of accuracy that will be achievable by the modelling approach adopted. In this paper, a new metric for the estimation of large-signal model accuracy is discussed, which is simply based on the comparison between deembedded measurements and model predictions of small-signal Y-parameters versus the bias voltages at the intrinsic device ports.

A Small-Signal Parameter-Based Metric for Nonlinear Models of Electron Devices

An experimental investigation of the low-frequency dispersion affecting the behaviour of microwave devices is reported in this work The study has been carried out by exploiting two different measurement techniques and experiments have been performed on a GaN based HEMT In particular, bias and frequency dependence of dynamic characteristics has been clearly observed Moreover, asymmetric behaviour not exclusively ascribed to the measurement environment (eg, termination impedance networks) manifests in the non-linear response of the considered device

Experimental investigation of LF dispersion and IMD asymmetry within GaN based HEMT technology

A nonlinear multi-bias model oriented to accurately predict the effects of charge-trapping in Gallium Nitride (GaN) HEMTs is proposed. As a case study, we considered a 0.25-µm 8×75-µm GaN HEMT. The model is identified by using CW low-frequency time-domain data and validated through high-frequency vector nonlinear measurements.

https://ieeexplore.ieee.org/iel7/7155722/7166703/07166857.pdf

Giorgio Vannini

Papers

A neural network approach for nonlinear modelling of LDMOSFETs

Influence of the gate current dynamic behaviour on GaAs HEMT reliability issues

A Small-Signal Parameter-Based Metric for Nonlinear Models of Electron Devices

Experimental investigation of LF dispersion and IMD asymmetry within GaN based HEMT technology

A new description of fast charge-trapping effects in GaN FETs