Behavioral Model for RF Power Amplifiers with Dynamic Transmission Based on Pattern Sensing and Deep Neural Network

Tong Tong, Yinshuang Zhao, Yucheng Yu, Xinyu Zhou, Peng Chen, Chao Yu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

This paper proposes a novel behavioral model of power amplifiers (PAs) operated with multiple states based on a pattern sensing module and a deep neural network (DNN) module. The results of the pattern sensing module are taken as the input of the DNN. Different PA behaviors can be effectively recognized by sensing the amplitude-modulation to amplitude-modulation (AM-AM) patterns and then quickly modelled by one DNN. Experimental results show that PA working in multiple states can be modelled by the proposed method with good performance without updating or selecting model coefficients.

Original languageEnglish
Title of host publication2023 International Conference on Microwave and Millimeter Wave Technology, ICMMT 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-3
Number of pages3
ISBN (Electronic)9798350338874
DOIs
Publication statusPublished - May 2023
Event15th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2023 - Qingdao, China
Duration: 14 May 202317 May 2023

Publication series

Name2023 International Conference on Microwave and Millimeter Wave Technology, ICMMT 2023 - Proceedings

Conference

Conference15th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2023
Country/TerritoryChina
CityQingdao
Period14/05/2317/05/23

Keywords

  • behavioral model
  • pattern sensing
  • power amplifiers (PAs)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Instrumentation
  • Radiation

Fingerprint

Dive into the research topics of 'Behavioral Model for RF Power Amplifiers with Dynamic Transmission Based on Pattern Sensing and Deep Neural Network'. Together they form a unique fingerprint.

Cite this