Volume 31, Issue 7 e22661
RESEARCH ARTICLE

Research on digital predistortion technology based on improved NLMS algorithm

Xin Wang

Xin Wang

School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou, China

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Simin Li

Simin Li

School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou, China

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Jincai Ye

Jincai Ye

School of Information Engineering, Guilin University of Electronic Technology, Guilin, China

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Xiangsuo Fan

Xiangsuo Fan

School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou, China

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Mimi Qin

Mimi Qin

School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou, China

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Xiaobo Jia

Xiaobo Jia

School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou, China

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Guofu Wang

Corresponding Author

Guofu Wang

School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou, China

Correspondence

Guofu Wang, Guangxi University of Science and Technology, Liuzhou, China.

Email: [email protected]

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Qingzheng Liu

Qingzheng Liu

School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou, China

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First published: 10 April 2021
Citations: 1

Funding information: National Natural Science Foundation of China, Grant/Award Number: 61761009; Natural Science Foundation of Guangxi Province, Grant/Award Number: 2020GXNSFAA297018

Abstract

In modern communication systems, digital predistortion (DPD) improve the linearity of PA and reduce the influence of nonlinear behavior on signal transmission. When identifying the inverse model of PA with traditional variable step size algorithm, the rate of convergence is slower, easy to be effected by noise and convergence instability exists at the optimal solution, which affects the optimization degree of the whole predistortion system. In this article, a digital predistortion technique based on improved NLMS algorithm is proposed to improve the accuracy of inverse model identification, convergence rate and anti-noise performance of the predistorter by changing the iteration function and introducing the autocorrelation matrix of the error of adjacent moments. The instability of the system at the optimal solution is reduced by introducing a new weight coefficient to update the correlation term. The simulation results show that the anti-noise performance, convergence rate and stability of the predistortion system based on New variable step size NLMS algorithm(NVNLMS) are obviously better than NLMS, and the out-of-band suppression of predistortion is optimized by 10 dB compared with the original system EVM is improved by 0.0031, ACPR is improved 9.1 dB.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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