A piecewise generalized memory polynomial model for envelope tracking power amplifiers
A piecewise generalized memory polynomial model for envelope tracking power amplifiers
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DOI：
10.1109/apmc.2015.7413559
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发表时间：
2015-12
期刊：
影响因子：
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通讯作者：
Chunlei Zhang;Jingqi Wang;Wen Wu
中科院分区：
文献类型：
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作者：
Chunlei Zhang;Jingqi Wang;Wen Wu
提出了一种用于包络跟踪功率放大器(ETPA)的分段广义记忆多项式模型。首先，利用向量阈值分解技术将输入的复包络信号分解为若干子信号，并利用广义记忆多项式(GMP)模型分别对每个子信号进行处理。然后，详细讨论了为获得最佳建模精度而选择的阈值。仿真结果表明，与传统的记忆多项式(MP)模型相比，在模型系数相同的情况下，该模型可使NMSE改善约3dB，并能准确地描述ET系统的不同特性。
This paper proposes a novel piecewise generalized memory polynomial model for envelope tracking power amplifiers (ETPAs). First, the input complex envelope signal is decomposed into several sub-signals by employing vector threshold decomposition technique and each sub-signal is processed separately by using generalized memory polynomial(GMP) model. Then, the selection of thresholds is discussed in detail to achieve optimum modeling accuracy. Simulation results show that the proposed model can provide about 3dB improvement of NMSE compared to the traditional memory polynomial(MP) model with same number of model coefficients, and can accurately describe the distinct characteristics of the ET system.