Combining Optical and Digital Compensation: Neural Network-Based Channel Equalisers in Dispersion-Managed Communications Systems

Karina Nurlybayeva, Morteza Kamalian-Kopae, Elena Turitsyna, Sergei K. Turitsyn

Research output: Contribution to journalArticlepeer-review

Abstract

Machine learning methods, including artificial neural networks, used for mitigation of nonlinear transmission impairments in ultra-long-haul and long-haul unmanaged fibre-optic links feature high complexity due to the accumulated dispersion resulting into large channel memory. Combination of the all-optical dispersion management techniques reducing effective channel memory and low-complexity digital post-processing potentially can offer an attractive trade-off between performance, complexity and costs (or power consumption). This paper demonstrates a feasibility of substantial complexity reduction of machine learning-based channel equalisation in dispersion-managed transmission with acceptable system performance.
Original languageEnglish
Pages (from-to)1-6
Number of pages6
JournalJournal of Lightwave Technology
DOIs
Publication statusE-pub ahead of print - 25 Mar 2024

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Keywords

  • Artificial neural networks
  • Complexity theory
  • Dispersion
  • Equalizers
  • Erbium-doped fiber amplifiers
  • Fiber nonlinear optics
  • Neural networks
  • Optical distortion
  • dispersion management
  • nonlinear equaliser
  • optical communications

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