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Enhanced Hammerstein Behavioral Model for Broadband Wireless Transmitters

机译:宽带无线发射机的增强型Hammerstein行为模型

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摘要

A novel enhanced Hammerstein behavior model consisting of a weighted memoryless polynomial followed by a Volterra filter is proposed. The weighted polynomial is used for predicting the strong static nonlinear behaviors of the power amplifiers (PAs). Since the Volterra filter is employed only for the mild dynamic nonlinearities, the filter can be implemented with low nonlinear order. Thus, this proposed model is capable of predicting both the static and dynamic nonlinearities of RF PAs with the acceptable complexity. The modeling performance of the proposed model is assessed in terms of in-band and out-of-band errors, such as normalized mean square error and adjacent channel error power ratio, and it is compared with a conventional Hammerstein, an augmented Hammerstein, and a Volterra series with respect to computation complexities such as the number of floating point operations and coefficients. The excellent estimation capability of the enhanced Hammerstein model is validated by two kinds of PAs: Si lateral diffusion metal–oxide–semiconductor and GaN high electron-mobility transistor amplifiers. Furthermore, the proposed scheme is applied to the digital predistortion (DPD) to cancel the nonlinearities of the PAs. The modeling performances and DPD experimental results clearly demonstrate the superiority of the enhanced Hammerstein scheme: the computational complexity is comparable with the augmented Hammerstein behavioral model, but the modeling performance is similar to the Volterra filter, which is the most accurate model.
机译:提出了一种新颖的增强型Hammerstein行为模型,该模型由加权无记忆多项式和Volterra滤波器组成。加权多项式用于预测功率放大器(PA)的强静态非线性行为。由于Volterra滤波器仅用于温和的动态非线性,因此可以以低非线性阶数实现滤波器。因此,该提出的模型能够以可接受的复杂度预测RF PA的静态和动态非线性。根据带内和带外误差(例如归一化均方误差和相邻信道误差功率比)评估了所提出模型的建模性能,并将其与常规Hammerstein,增强型Hammerstein和关于计算复杂性(例如浮点运算的数量和系数)的Volterra级数。两种类型的功率放大器验证了增强型Hammerstein模型的出色估计能力:Si横向扩散金属氧化物半导体和GaN高电子迁移率晶体管放大器。此外,将所提出的方案应用于数字预失真(DPD)以消除功率放大器的非线性。建模性能和DPD实验结果清楚地证明了增强型Hammerstein方案的优越性:计算复杂度可与增强型Hammerstein行为模型相媲美,但建模性能类似于最精确的模型Volterra滤波器。

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