Iterative Learning Control Assisted Neural Network for Digital Predistortion of MIMO Power Amplifier

Kenan Li, Ning Guan, Hua Wang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Digital predistortion (DPD) technique has already been studied and applied in traditional SISO system. However, when it comes to multiple-input multiple- output (MIMO) systems, new problems such as crosstalk effects must be taken into consideration. In this paper, we propose a neural network based DPD scheme assisted by iterative learning control (ILC) for MIMO system. The proposed method uses direct learning architecture in DPD and can combat nonlinear crosstalk effects. Simulation results show that the proposed method achieves similar or even better performance than the existing polynomial based approach in the presence of nonlinear crosstalk. In addition, the proposed method is computational efficient and is easier for implementation.

Original languageEnglish
Title of host publication2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538663554
DOIs
Publication statusPublished - 20 Jul 2018
Event87th IEEE Vehicular Technology Conference, VTC Spring 2018 - Porto, Portugal
Duration: 3 Jun 20186 Jun 2018

Publication series

NameIEEE Vehicular Technology Conference
Volume2018-June
ISSN (Print)1550-2252

Conference

Conference87th IEEE Vehicular Technology Conference, VTC Spring 2018
Country/TerritoryPortugal
CityPorto
Period3/06/186/06/18

Keywords

  • Digital predistortion
  • Iterative learning control
  • MIMO
  • Neural network
  • Power amplifier

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