A Residual-Fitting Modeling Method for Digital Predistortion of Broadband Power Amplifiers

DOI number:10.1109/LMWC.2022.3162759

Affiliation of Author(s):中国科学技术大学电子工程与信息科学系

Journal:IEEE Microwave and Wireless Components Letters

Funded by:国家自然科学基金 61471333

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

Abstract: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).

First Author:Yufei Suo (锁宇飞)

Co-author:Wen Qiao,Chengye Jiang,Boyang Zhang

Indexed by:Journal paper

Correspondence Author:Falin Liu

Discipline:Engineering

Document Type:J

Volume:32

Issue:9

Page Number:1115-1118

Translation or Not:no

Date of Publication:2022-09-01

Included Journals:SCI、EI