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A comparison of indirect learning and closed loop estimators used in digital predistortion of power amplifiers

机译:功率放大器数字预失真中使用的间接学习和闭环估计器的比较

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Indirect learning is often used as an estimator in digital predistortion of power amplifiers (PAs). The estimator has inherent flaws that become apparent when signal bandwidths increase. These include coefficient offsets, excessive ADC sampling requirements, and susceptibility to EVM and PA saturation. A comparison to the closed loop estimator shows that these flaws are specific to the indirect learning estimator.
机译:间接学习通常用作功率放大器(PA)的数字预失真中的估计器。估计器具有内在的缺陷,当信号带宽增加时,这些缺陷会变得明显。其中包括系数偏移,过多的ADC采样要求以及对EVM和PA饱和的敏感性。与闭环估计器的比较表明,这些缺陷特定于间接学习估计器。

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