DOI码：10.1109/LMWC.2022.3162759

所属单位：中国科学技术大学电子工程与信息科学系

发表刊物：IEEE Microwave and Wireless Components Letters

项目来源：国家自然科学基金 61471333

关键字：Behavioral modeling, broadband power amplifier (PA), digital predistortion (DPD), neural network (NN), residual-fitting.

摘要：This letter proposes a residual-fitting modeling method for digital predistortion (DPD) of broadband power amplifiers (PAs), and then constructs a residual-fitting model. To avoid directly modeling strong nonlinearity and memory effect, the model is split into a conversion, fitting, and recovery module. In this way, the nonlinearity and memory effect of the output signal of PAs are reduced after the conversion module, and then the fitting module models the converted signal, finally the behavioral characteristics of PAs are recovered by the recovery module. In the experimental test, a 100 MHz orthogonal frequency division multiplexing (OFDM) signal is used as input signal of a Doherty PA. The experimental results show that compared with the existing augmented real-valued time-delay neural network (ARVTDNN), the proposed residualfitting memory polynomial-ARVTDNN (MP-ARVTDNN) model with much fewer coefficients lowers normalized mean square error (NMSE) and adjacent channel power ratio (ACPR).

合写作者：Wen Qiao,Chengye Jiang,Boyang Zhang

第一作者：Yufei Suo (锁宇飞)

论文类型：期刊论文

通讯作者：Falin Liu

学科门类：工学

文献类型：J

卷号：32

期号：9

页面范围：1115-1118

是否译文：否

发表时间：2022-09-01

收录刊物：SCI、EI