Comparison of evaluation criteria for power amplifier behavioral modeling

Abstract

In this paper different evaluation criteria for power amplifier behavioral modeling are studied and evaluated using measuremed data. The figure-of-merits are calculated from complex-envelope data of a sampled power amplifier intended for 3G. Both time-and frequency domain methods are included in the study. It is found that a model evaluation criterion should have ability to capture both the linear and nonlinear distortion as well as the memory effects in the power amplifier. The normalized mean square error (NMSE) and the weighted error-to-signal power ratio (WE-SPR) are found to be the strongest candidates for capturing the in-band and the out-of-band errors, respectively. Both are also independent of power amplifier technology and stimuli input.

Memory models for behavioral modeling and digital predistortion of envelope tracking power amplifiers

“…(14)). The model evaluation is performed using two of the most used figures of merit for the evaluation of PA models and predistorters [31]: the Normalized Mean Squared Error (NMSE) and the Adjacent Channel Error Power Ratio (ACEPR). NMSE can be defined as:…”

“…Normalized Root-Mean-Square Error (NRMSE) can be used to evaluate model and linearizers errors from time-domain signals, but here the NMSE is used because it can be directly related to EVM (Error Vector Magnitude) [31,32]. Moreover NMSE provides a more straightforward comparison with results reported in the state-of-the-art of PA modeling and DPD, where the NMSE is widely adopted [33,31]. The model error estimated using NMSE is mostly dominated by the in-band error of the model [31].…”

“…Moreover NMSE provides a more straightforward comparison with results reported in the state-of-the-art of PA modeling and DPD, where the NMSE is widely adopted [33,31]. The model error estimated using NMSE is mostly dominated by the in-band error of the model [31]. Therefore a complementary figure of merit is needed in order to accurately evaluate the PA model out-of-band error.…”

An Accurate Complexity-Reduced Simplified Volterra Series for Rf Power Amplifiers

“…This paper also presents a systematic comparison between the proposed ACR-SV model and different stateof-the-art models, such as the MP model, SV model and ACR-GMP model. Normalized mean square error (NMSE) is considered as an in-band distortion metric, while adjacent channel power ratio (ACPR) is taken to be a spectral regrowth benchmark [18,19]. The comparison results fully illustrate that the ACR-SV model is superior to the other three models in terms of accuracy and complexity.…”

Accurate estimation of memory polynomial model dimension for dynamic nonlinear RF power amplifiers

“…The time domain metrics mainly include the normalized mean squared error (NMSE), the memory effects ratio (MER), and the memory effects modeling ratio (MEMR) [9] [10]. In frequency domain, the adjacent channel error power ratio (ACEPR), and the weighted error-tosignal power ratio (WESPR) are among the metrics commonly used to evaluate the performance of behavioral models [11] [12].…”