Neural network modeling of bismuth-doped fiber amplifier

Aleksandr Donodin*, Uiara Celine De Moura, Ann Margareth Rosa Brusin, Egor Manuylovich, Vladislav Dvoyrin, Francesco Da Ros, Andrea Carena, Wladek Forysiak, Darko Zibar, Sergei K. Turitsyn

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Bismuth-doped fiber amplifiers offer an attractive solution for meeting continuously growing enormous demand on the bandwidth of modern communication systems. However, practical deployment of such amplifiers require massive development and optimization efforts with the numerical modeling being the core design tool. The numerical optimization of bismuth-doped fiber amplifiers is challenging due to a large number of unknown parameters in the conventional rate equations models. We propose here a new approach to develop a bismuth-doped fiber amplifier model based on a neural network purely trained with experimental data sets in E- and S-bands. This method allows a robust prediction of the amplifier operation that incorporates variations of fiber properties due to manufacturing process and any fluctuations of the amplifier characteristics. Using the proposed approach the spectral dependencies of gain and noise figure for given bi-directional pump currents and input signal powers have been obtained. The low mean (less than 0.19 dB) and standard deviation (less than 0.09 dB) of the maximum error are achieved for gain and noise figure predictions in the 1410- 1490 nm spectral band.

Original languageEnglish
Article number4
JournalJournal of the European Optical Society-Rapid Publications
Volume19
Issue number1
ISSN1990-2573
DOIs
Publication statusPublished - 2023

Keywords

  • Amplifier
  • Bismuth
  • Doped fiber
  • Multi-band
  • Neural network
  • Optical communications
  • Optical networks
  • Ultra-wideband

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