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

Zhu, Anding; Pedro, José; Brazil, T.J.

doi:10.1109/tmtt.2006.883243

Cited by 396 publications

(228 citation statements)

References 12 publications

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“…Thus, the Volterra model exhibits more accurate and stable performance than the LSSVM model for low-frequency components, which reflect the overall physical characteristics of the system. As such, the Volterra model can clearly describe the physical meaning of the system and accurately predict the continuous function [46].…”

Section: The Forecasting Results For Hourly Ghi Using the Lssvm And Tmentioning

confidence: 99%

Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition

et al. 2018

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Accurate solar forecasting facilitates the integration of solar generation into the grid by reducing the integration and operational costs associated with solar intermittencies. A novel solar radiation forecasting method was proposed in this paper, which uses two kinds of adaptive single decomposition algorithm, namely, empirical mode decomposition (EMD) and local mean decomposition (LMD), to decompose the strong non-stationary solar radiation sequence into a set of simpler components. The least squares support vector machine (LSSVM) and the Volterra model were employed to build forecasting sub-models for high-frequency components and low-frequency components, respectively, and the sub-forecasting results of each component were superimposed to obtain the final forecast results. The historical solar radiation data collected on Golden (CO, USA), in 2014 were used to evaluate the accuracy of the proposed model and its comparison with that of the ARIMA, the persistent model. The comparison demonstrated that the superior performance of the proposed hybrid method.

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Section: The Forecasting Results For Hourly Ghi Using the Lssvm And Tmentioning

confidence: 99%

Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition

et al. 2018

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“…The reduced-complexity technology removes some items which contribute to modeling accuracy rarely. The effects of nonlinear dynamics tend to fade with increasing order in many real PAs [11,12], and we can attempt to adjust the maximum nonlinearity order of the previous input to decrease the number of coefficients and achieve accurate behavioral model of PAs.…”

Section: Mgmp Modelmentioning

confidence: 99%

A Modified Generalized Memory Polynomial Model for Rf Power Amplifiers

Sun¹,

Yu²,

Liu³

et al. 2014

PIER Letters

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Abstract-A modified generalized memory polynomial model (MGMP) is proposed for RF power amplifiers (PAs). The MGMP model is derived by applying complexity-reduced technique to the generalized memory polynomial model (GMP), and the least square (LS) algorithm is used for coefficient extraction. The proposed MGMP model is assessed using a GaN Class-F PA driven by two modulated signals (a WCDMA 1001 signal and a single carrier 16QAM signal with 20 MHz bandwidth). The experimental results demonstrate that the MGMP model outperforms the memory polynomial (MP) model and the generalized memory polynomial (GMP) model. Compared with MP model, the MGMP model shows a normalized mean square error (NMSE) improvement of 2.13 dB in forward modeling, average adjacent channel power ratio (ACPR) improvement of 2.62/2.11 dB in the DPD application with almost identical number of model coefficients. In contrast with the GMP model, the MGMP model can achieve comparable forward modeling and linearization performance results, but reduces approximately 40% of coefficients.

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“…The classical Volterra-series for instance can only be used to characterize weakly nonlinear systems [3]. Furthermore thermal analysis of high power GaN amplifiers, show that thermal resistance significantly changes depending on operating conditions [4].…”

Section: Electro-thermal Models Reviewedmentioning

confidence: 99%

Measurement technique to assess the impact of RF power amplifier memory effects on radar performance

Tua¹

2012

2012 IEEE Radar Conference

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

Cited by 396 publications

References 12 publications

Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition

Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition

A Modified Generalized Memory Polynomial Model for Rf Power Amplifiers

Measurement technique to assess the impact of RF power amplifier memory effects on radar performance

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