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Successive Quantization of the Neural Network Equalizers in Optical Fiber Communication

  • Telecom Paris
  • Infinera Portugal
  • Infinera

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

Abstract

A pragmatic successive quantization approach is applied to a neural network equalizer in a 16-QAM dual-polarization fiber transmission experiment over a 9x50km TWC fiber link. Quantization at 5 bits reduces the complexity by 85%, with a negligible Q-factor penalty.

Original languageEnglish
Title of host publication2023 Opto-Electronics and Communications Conference, OECC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665462136
DOIs
Publication statusPublished - 1 Jan 2023
Event2023 Opto-Electronics and Communications Conference, OECC 2023 - Shanghai, China
Duration: 2 Jul 20236 Jul 2023

Publication series

Name2023 Opto-Electronics and Communications Conference, OECC 2023

Conference

Conference2023 Opto-Electronics and Communications Conference, OECC 2023
Country/TerritoryChina
CityShanghai
Period2/07/236/07/23

Keywords

  • Optical fiber communication
  • neural network equalization
  • nonlinearity mitigation
  • quantization

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