Complexity-Reduced Neural Network for Behavioral Modeling and Digital Predistortion of RF Wireless Transmitters

DOI number:10.1109/TIM.2025.3574905

Affiliation of Author(s):图书馆VIP信息学院

Teaching and Research Group:电子工程与信息科学系

Journal:IEEE Transactions on Instrumentation and Measurement (Early Access)

Funded by:NNSF 62371436

Key Words:Digital predistortion (DPD), neural network, piecewise function, power amplifier (PA).

Abstract:Neural networks (NNs) are promising for behavioral modeling and compensation of complicated nonlinearities in 5G transmitters with broadband high-efficiency structural power amplifiers (PAs). The high computational complexity of NNs, however, poses a serious challenge to their practical imple mentation. In response to this challenge, a complexity-reduced NN (CR-NN) approach is proposed in this paper, which builds on the relationship between data features and model capacity. Considering the memory fading property of RF PAs, the CR NN first utilizes post-filtering to significantly reduce the input features of the NN body. This is followed by the employment of adaptive non-uniform piecewise linear unit to improve the model capacity without increasing the complexity. In order to validate the proposed method, the experiments are carried out based on a two-stage Doherty PA and a GaN-based Doherty PA. Experimental results show that the proposed CR-NN method can suppress the strong nonlinearity of PA from-23.24/-24.34 dBc to -51.18/-50.72 dBc with a computational complexity comparable to that of the linear-in-parameters models, thus demonstrating that the proposed method can significantly improve the performance complexity tradeoff of NN-DPDs and contribute to the linearization of 5G and future systems.

First Author:Chengye Jiang

Co-author:Qianqian Zhang,Junsen Wang,Junning Zhang,Kunfeng Zhang

Indexed by:Journal paper

Correspondence Author:Bo Tang,Falin Liu

Document Code:10.1109/TIM.2025.3574905

Discipline:Engineering

Document Type:J

Volume:Online

Issue:Online

Page Number:1-15

Translation or Not:no

Date of Publication:2025-05-31