Behavior Modeling of Broadband Power Amplifier Based on Weighted Two Box Model

doi:10.3969/j.issn.0372-2112.2018.06.012

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Behavior Modeling of Broadband Power Amplifier Based on Weighted Two Box Model

更新时间：2025-07-08

- Behavior Modeling of Broadband Power Amplifier Based on Weighted Two Box Model
- Acta Electronica Sinica Vol. 46, Issue 6, Pages: 1357-1364(2018)
- 作者机构：
  
  海军工程大学舰船综合电力技术国防科技重点实验室,湖北,武汉,430033
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China for Excellent Youth Schoolars (No.51422705)
- DOI：10.3969/j.issn.0372-2112.2018.06.012
  CLC： TN919
- Published Online：25 June 2018，
  
  Published：2018
- 稿件说明：
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Behavior Modeling of Broadband Power Amplifier Based on Weighted Two Box Model[J]. Acta Electronica Sinica, 2018, 46(6): 1357-1364.
DOI：

Behavior Modeling of Broadband Power Amplifier Based on Weighted Two Box Model[J]. Acta Electronica Sinica, 2018, 46(6): 1357-1364. DOI： 10.3969/j.issn.0372-2112.2018.06.012.

摘要

针对宽带功放行为建模和畸变补偿问题，提出了一种新的权重双盒模型.该模型采用无记忆子模型和记忆子模型权重级联的方法分别对宽带功放的静态非线性和动态非线性进行级联建模.首先，利用无记忆子模型对输入信号幅值进行压缩，降低静态畸变部分模型的拟合误差，并在子模型输出端进行解权值，保证功放的饱和驱动特性；接着，构建权重记忆子模型，利用信号幅值相关的权值函数动态的调整高低功率动态畸变子模型的权重比例.实验结果表明，在不同信号驱动情况下，本文方法在降低计算复杂度的同时，保持了更好的建模精度和畸变补偿能力.

Abstract

This paper proposes a weighted two-box model to solve the behavior modeling and distortion compensation problem of broadband RF power amplifier.The model

including the memoryless sub-model and memory sub-model

is used to model the static nonlinear and dynamic nonlinear of broadband power amplifiers.First of all

the input signal amplitude is compressed in weighted memoryless sub-model

which can effectively reduce the fitting error of the static distortion model.And the output value of the sub model is decompressed to ensure the saturation driving performance of the amplifier.Second

the weight memory sub-model is constructed

and is used to adjust the low and high power dynamic distortion sub model dynamically based on the weight function related to signal amplitude.The experimental results show that under different signal driving conditions

the proposed method can reduce the computational complexity while maintain better modeling accuracy and distortion compensation capability.

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