• DocumentCode
    120719
  • Title

    Fibre impairment compensation using artificial neural network equalizer for high-capacity coherent optical OFDM signals

  • Author

    Jarajreh, Mutsam A. ; Rajbhandari, Sujan ; Giacoumidis, E. ; Doran, N.J. ; Ghassemlooy, Zabih

  • Author_Institution
    Opt. Commun. Res. Group, Univ. of Northumbria at Newcastle, Newcastle upon Tyne, UK
  • fYear
    2014
  • fDate
    23-25 July 2014
  • Firstpage
    1112
  • Lastpage
    1117
  • Abstract
    We propose an artificial neural network (ANN) equalizer for transmission performance enhancement of coherent optical OFDM (C-OOFDM) signals. The ANN equalizer showed more efficiency in combating both chromatic dispersion (CD) and single-mode fibre (SMF)-induced non-linearities compared to the least mean square (LMS). The equalizer can offer a 1.5 dB improvement in optical signal-to-noise ratio (OSNR) compared to LMS algorithm for 40 Gbit/s C-OOFDM signals when considering only CD. It is also revealed that ANN can double the transmission distance up to 320 km of SMF compared to the case of LMS, providing a nonlinearity tolerance improvement of ~0.7 dB OSNR.
  • Keywords
    OFDM modulation; equalisers; neural nets; optical fibre communication; optical modulation; telecommunication computing; ANN; C-OOFDM; LMS; OSNR; artificial neural network equalizer; bit rate 40 Gbit/s; chromatic dispersion; fibre impairment compensation; high-capacity coherent optical OFDM signals; least mean square; nonlinearity tolerance improvement; optical signal-to-noise ratio; single-mode fibre; transmission performance enhancement; Artificial neural networks; Equalizers; OFDM; Optical fibers; Optical noise; Signal to noise ratio; Coherent Optical OFDM; Optical fibre communication; artificial neural networks; functional link; nonlinear channel equalization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communication Systems, Networks & Digital Signal Processing (CSNDSP), 2014 9th International Symposium on
  • Conference_Location
    Manchester
  • Type

    conf

  • DOI
    10.1109/CSNDSP.2014.6923996
  • Filename
    6923996