Improved performance in the detection of aco-ofdm modulated signals using deep learning modules

Laialy Darwesh, Natan Kopeika

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Free space optical communication (FSO) is widely deployed to transmit high data rates for rapid communication traffic increase. Asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) modulation is a very efficient FSO communication technique in terms of transmitted optical power. However, its performance is limited by atmospheric turbulence. When the channel includes strong turbulence or is non-deterministic, the bit error rate (BER) increases. To reach optimal performance, the ACO-OFDM decoder needs to know accurate channel state information (CSI). We propose novel detection using different deep learning (DL) algorithms. Our DL models are compared with minimum mean square error (MMSE) detection methods in different turbulent channels and improve performance especially for non-stationary and non-deterministic channels. Our models yield performance very close to that of the MMSE estimator when the channel is characterized by weak or strong turbulence and is stationary. However, when the channel is non-stationary and variable, our DL model succeeds in improving the performance of the system and decreasing the signal to noise ratio (SNR) by more than 8 dB compared to that of the MMSE estimator, and it succeeds in recovering the received data without needing to know accurate CSI. Our DL decoders also show notable speed and energy efficiency improvement.

Original languageEnglish
Article number8380
Pages (from-to)1-14
Number of pages14
JournalApplied Sciences (Switzerland)
Volume10
Issue number23
DOIs
StatePublished - 1 Dec 2020

Keywords

  • Additive white gaussian noise
  • Asymmetrically clipped optical orthogonal frequency division multiplexing
  • Channel state information
  • Free space optical communication
  • Fully connected neural network
  • Fully convolutional neural networks
  • Minimum mean square error

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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