Neural Network Based Digital Pre-Distorter Design for DCO-OFDM Visible Light Communications

Omer Narmanlioglu, Bugra Turan, Sinem Coleri, Murat Uysal

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

Abstract

Direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) is an appealing modulation scheme for reliable, high-speed optical transmissions and foreseen to be used in the upcoming IEEE 802.11bb visible light communication (VLC) standard. However, non-linear characteristics of light emitting diodes (LEDs) as VLC transmitters degrade the bit error rate (BER) performance of DCO-OFDM due to its high peak-to-average-power ratio. In this paper, we propose neural network based digital pre-distorter (DPD) to mitigate non-linear LED response for DCO-OFDM transmission. The proposed scheme extends the reliable transmission range by 1.75 m and 1.8 cm for non-compensated LED response and Memory Polynomial based DPD, respectively.

Original languageEnglish (US)
Title of host publication2022 IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages142-147
Number of pages6
ISBN (Electronic)9781665498258
DOIs
StatePublished - 2022
Event2nd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2022 - Athens, Greece
Duration: Sep 5 2022Sep 8 2022

Publication series

Name2022 IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2022

Conference

Conference2nd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2022
Country/TerritoryGreece
CityAthens
Period9/5/229/8/22

Keywords

  • DCO-OFDM
  • Visible light communication
  • digital pre-distorter
  • linearization
  • neural network

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Control and Optimization

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