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Experimental sensitivity analysis of multi-standard power amplifiers nonlinear characterization under modulated signals

机译:多标准功率放大器调制信号非线性表征的实验灵敏度分析

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摘要

This paper proposes an experimental analysis focusing on the sensitivity of three behavioral models, Memory Polynomial (MP), Augmented Hammerstein (AH) and the two hidden layers artificial neural networks (2HLANN) to the characteristics of the input signal driving the power amplifier (PA) to be linearized. The analysis is carried out by changing separately each signal characteristic, respectively the peak to average power ratio (PAPR), the Probability density function (PDF), and the modulation bandwidth and assess the sensitivity of the DPD to that change. When used to linearise a 250 Watt peak-envelop-power Doherty PA, the considered models showed relatively small sensitivity to the variation of these signal characteristics. Yet, the 2HLANN was found to be the most robust model with excellent linearization capabilities.
机译:本文针对3种行为模型(记忆多项式(MP),增强Hammerstein(AH)和两个隐藏层人工神经网络(2HLANN))对驱动功率放大器(PA)的输入信号特性的敏感性进行了实验分析)进行线性化。通过分别更改每个信号特征(分别是峰均功率比(PAPR),概率密度函数(PDF)和调制带宽)并评估DPD对该变化的敏感性来进行分析。当用于线性化250瓦峰值包络功率Doherty PA时,所考虑的模型对这些信号特性的变化显示出相对较小的灵敏度。然而,发现2HLANN是具有出色线性化功能的最强大的模型。

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