A pruning method of feedforward neural network based on contribution of input components for digital predistortion of power amplifier. Issue 1 (29th September 2022)

• Record Type: Journal Article
• Authors: Zhao, Guobo; Wang, Guizhen; Lin, Yingchao; Li, Shulan; Yu, Cuiping; Liu, Yuanan
• Is Part Of: Microwave and optical technology letters. Volume 65:Issue 1(2023)
• Page Start: 98
• Page End: 103
• Publication Date: 2022-09-29
• Subjects: behavior model -- feedforward neural network (FNN) -- power amplifiers (PAs) -- pruning; Microwaves -- Periodicals; Optics -- Periodicals; 621
• DOI: 10.1002/mop.33465
• Languages: English
• ISSNs: 0895-2477

Abstract

Abstract: In this paper, a pruning method based on contribution of input components is proposed to optimize the structure of Feedforward neural network (FNN) model for digital predistortion of power amplifier. The optimized hyperparameters are the specific input components. The pruning algorithm is divided into two stages. The stage I is used to rough rank the importance of input components and the stage II is used to modify the results of stage I. Then, we can get an importance ranking of input components. By deleting unimportant input components, a pruned FNN model with fewer input components can be obtained. Test results show that the proposed algorithm can greatly simplify the FNN model on the premise of ensuring the predistortion performance.