Nonlinear Impairment Compensation Using Neural Networks

Shinsuke Fujisawa, Fatih Yaman, Hussam G. Batshon, Massaki Tanio, Naoto Ishii, Chaoran Huang, Thomas Ferreira De Lima, Yoshihisa Inada, Paul R. Prucnal, Norifumi Kamiya, Ting Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Neural networks are attractive for nonlinear impairment compensation applications in communication systems. In this paper, several approaches to reduce computational complexity of the neural network-based algorithms are presented.

Original languageEnglish (US)
Title of host publication2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781943580866
StatePublished - Jun 2021
Event2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - San Francisco, United States
Duration: Jun 6 2021Jun 11 2021

Publication series

Name2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - Proceedings

Conference

Conference2021 Optical Fiber Communications Conference and Exhibition, OFC 2021
Country/TerritoryUnited States
CitySan Francisco
Period6/6/216/11/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Atomic and Molecular Physics, and Optics

Fingerprint

Dive into the research topics of 'Nonlinear Impairment Compensation Using Neural Networks'. Together they form a unique fingerprint.

Cite this