Magnitude Scaling-Based Behavioral Model for Power Amplifiers With Dynamic Power Transmission
Magnitude Scaling-Based Behavioral Model for Power Amplifiers With Dynamic Power Transmission
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DOI：
10.1109/lmwc.2021.3135889
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发表时间：
2022-05
影响因子：
3
通讯作者：
Jiayan Wu;Songbai He;Jun Peng;Peng Hao;F. You
中科院分区：
文献类型：
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作者：
Jiayan Wu;Songbai He;Jun Peng;Peng Hao;F. You
在这封信中，我们提出了一个基于幅度缩放的动态功率传输的功率可伸缩模型。该功率自适应模型利用了功率放大器（PA）在不同功率水平下的行为规律性，在数字预失真（DPD）过程中只需在低幅值处更新模型参数。采用功率因数对输入信号的幅值进行标定，采用分段模型实现参数的分段提取。在氮化镓（GaN）功率放大器上进行的实验表明，所提出的方法可以减少至少50%的模型系数需要更新的可比线性化性能。
In this letter, we present a magnitude scaling-based power scalable model for dynamic power transmission. Benefiting from the regularity of power amplifier’s (PA’s) behavior at different power levels, the proposed power adaptive model only needs to update the model parameters in the low magnitude during digital predistortion (DPD). A power factor is applied to scale the magnitude of the input signal, and a piecewise model is used to realize the segmented extraction of the parameters. Experiments carried out on a gallium nitride (GaN) PA show that the proposed method can reduce the model coefficients needed update by at least 50% with the comparable linearization performance.