A Broadband Dual-Inflection Point Rf Predistortion Linearizer Using Backward Reflection Topology

Hashmi, Mohammad S.; Rogojan, Zaharia S.; Nazifi, Saeed Rezaei; Ghannouchi, and Fadhel M.

doi:10.2528/pierc10032801

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…One of the most and cost effective linearization techniques to PA nonlinearity is digital predistortion (DPD) [1,2], which has many advantages such as high fidelity, efficiency, and easy realization of adaptive processing [3][4][5]. Moreover, the final purpose of realizing PA linearization digital predistortion needs behavioral modeling precisely, for which Volterra series comes to light.…”

Section: Introductionmentioning

confidence: 99%

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

Sun¹,

Yu²,

Liu³

et al. 2014

PIER C

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Abstract-An accurate complexity-reduced simplified Volterra (ACR-SV) series is introduced for RF power amplifiers (PAs). Based on the conventional simplified Volterra (SV) series, it takes memoryless nonlinearity and memory effect into consideration separately, while connected with a nonlinear memory effect (NME) in order to increase accuracy of the model. The proposed ACR-SV 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 40 MHz band width). The experimental results in forward modeling and DPD application demonstrate that the proposed ACR-SV model outperforms the memory polynomial (MP) model, the augmented complexity-reduced generalized memory polynomial (ACR-GMP), and the SV model. Compared with the MP model, the ACR-SV model shows a normalized mean square error (NMSE) improvement of 2.61 dB in forward modeling, average adjacent channel power ratio (ACPR) improvement of 3.7/4.2 dB in the DPD application with less 13% number of model coefficients. In comparison with the ACR-GMP model, the ACR-SV model shows NMSE improvement of 1.39 dB, ACPR improvement of 0.7/0.6 dB with comparable number of model coefficients. In contrast with the SV model, the ACR-SV model achieves similar model accuracy, but reduces approximately 53% of coefficients.

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Section: Introductionmentioning

confidence: 99%

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

Sun¹,

Yu²,

Liu³

et al. 2014

PIER C

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“…Accurate modeling offers effective prediction of PA nonlinearity and the inverse model as a digital predistorter is developed for the linearization [1][2][3]. The key goal of digital predistorter is to find a good model to approximate the inverse of the PA nonlinearity.…”

Section: Introductionmentioning

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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Compensation of Nonlinear Distortions in Telecommunication Systems with the Use of Functional Series of Volterra

Pirogova

Neches

2017

Advances in Intelligent Systems and Computing

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A Broadband Dual-Inflection Point Rf Predistortion Linearizer Using Backward Reflection Topology

Cited by 5 publications

References 17 publications

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

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

A Modified Generalized Memory Polynomial Model for Rf Power Amplifiers

Compensation of Nonlinear Distortions in Telecommunication Systems with the Use of Functional Series of Volterra

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