DOI码：10.1109/TIM.2025.3574905

所属单位：图书馆VIP信息学院

教研室：电子工程与信息科学系

发表刊物：IEEE Transactions on Instrumentation and Measurement (Early Access)

项目来源：NNSF 62371436

关键字：Digital predistortion (DPD), neural network, piecewise function, power amplifier (PA).

摘要：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.

第一作者：Chengye Jiang

合写作者：Qianqian Zhang,Junsen Wang,Junning Zhang,Kunfeng Zhang

论文类型：期刊论文

通讯作者：Bo Tang,Falin Liu

论文编号：10.1109/TIM.2025.3574905

学科门类：工学

文献类型：J

卷号：Online

期号：Online

页面范围：1-15

是否译文：否

发表时间：2025-05-31