Power Amplifier Behavioral Modeling Research Articles

Pruning the Volterra Series for Behavioral Modeling of Power Amplifiers Using Physical Knowledge

This paper presents an efficient and effective approach to pruning the Volterra series for behavioral modeling of RF and microwave power amplifiers. Rather than adopting a pure black-box approach, this model pruning technique is derived from a physically meaningful block model, which has a clear linkage to the underlying physical behavior of the device. This allows all essential physical properties of the PA to be retained, but significantly reduces model complexity by removing unnecessary coefficients from the general Volterra series. A reduced-order model of this kind can be easily extracted from standard time/frequency-domain measurements or simulations, and may be simply implemented in system-level simulators. A complete physical analysis and a systematic derivation are presented, together with both computer simulations and experimental validations

Behavioral modeling of power amplifiers for wireless applications

In this paper, an improved empirical behavioral model for radio-frequency power amplifiers (RF-PAs) is presented. The model was implemented in a commercial nonlinear microwave simulator. It belongs to the category of bandpass PA models, which exhibits memory effects due to the low frequency dependence of bias and temperature. Additionally, it facilitates accurate and efficient system level simulations of RF-PA large-signal behaviors such as self-bias, AM-AM, AM-PM, gain expansion effects, and intermodulation distortion (IMD) sweet-spots. The model was validated using measurement data obtained from a commercial CDMA PA at 1.88 GHz. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.

Neural networks and volterra series for time-domain power amplifier behavioral models

This paper presents a black-box model that can be applied to characterize the nonlinear dynamic behavior of power amplifiers (PAs), including strong nonlinearities and memory effects. Feedforward time-delay Neural Networks (TDNN) are used to extract the model from a large-signal input-output time-domain characterization in a given bandwidth; furthermore, explicit formulas to derive Volterra kernels from the TDNN parameters are also presented. The TDNN and related Volterra models can predict the amplifier response to different frequency excitations in the same bandwidth and power sweep. As a case study, a PA, characterized with a two-tone power swept excitation, is modeled and simulations are found in good agreement with training measurements; moreover, a model validation with two tones of different frequencies and spacing is also performed. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.

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

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

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

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.

Behavioral modeling of RF power amplifiers based on pruned volterra series

Behavioral modeling techniques provide a convenient and efficient means to predict system-level performance without the computational complexity of full circuit simulation or physics-level analysis of nonlinear systems, thereby significantly speeding up the analysis process. General Volterra series based models have been successfully applied for radio frequency (RF) power amplifier (PA) behavioral modeling, but their high complexity tends to limit their applications to "weakly" nonlinear systems. To model a PA with strong nonlinearities and long memory effects, for example, the general Volterra model involves a great number of coefficients. In this letter, we propose a new simplified Volterra series based model for RF power amplifiers by employing a "near-diagonality" pruning algorithm to remove the coefficients which are very small, or else not sensitive to the output error, therefore dramatically reducing the complexity of the behavioral model.