Deep Learning for Improving Performance of OOK Modulation over FSO Turbulent Channels

Laialy Darwesh, Natan Kopeika

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

Free space optical (FSO) communication technology has become increasingly advanced with capabilities of high speed, high capacity, and low power consumption. However, despite the great potential of FSO, its performance is limited in a turbulent atmosphere. Atmospheric turbulence causes scintillation in the FSO propagated signals, leading to an increase in the bit error rate (BER) performance of the recovered signals at the receiver. In this paper, we demonstrate that the use of deep learning (DL) detection methods could overcome these limitations. We present a new detection method of on-off keying (OOK) modulated signals by using different models of DL over different strength FSO turbulent channels, without the need for prior knowledge of the parameters of the channel. The demonstrated DL decoders improve the performance of the FSO turbulent channel and decrease the power consumption. Moreover, the demonstrated DL models also work faster than maximum likelihood (ML) methods with perfect channel estimation decoders, with even slightly better performance because of the turbulence, thus enabling realization of FSO over turbulent atmospheric channels.

Original languageEnglish
Article number9177273
Pages (from-to)155275-155284
Number of pages10
JournalIEEE Access
Volume8
DOIs
StatePublished - 1 Jan 2020

Keywords

  • Free space optical communication
  • additive white gaussian noise
  • amplitude shift keying modulation
  • bit error rate
  • channel state information
  • deep learning
  • direct detection
  • fully connected neural network
  • fully convolutional neural network
  • intensity modulation
  • maximum likelihood
  • on-off keying modulation
  • photodetector

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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