Neural network modeling of bismuth-doped fiber amplifier


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.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Electrical and Electronic Engineering
Additional Information: Funding Information: This work was funded from the UK EPSRC grants EP/R035342/1 and EP/V000969/1, the European Union's Horizon 2020 research and innovation programs under the Marie Skłodowska-Curie grant agreement 814276, 813144 and 754462, the Villum Foundations (VYI OPTIC-AI grant no. 29344), the European Research Council through the ERC-CoG FRECOM project (grant agreement no. 771878), and the Italian Ministry for University and Research (PRIN 2017, project FIRST). Publisher Copyright: © The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: Amplifier,Bismuth,Doped fiber,Multi-band,Neural network,Optical communications,Optical networks,Ultra-wideband,Atomic and Molecular Physics, and Optics
Publication ISSN: 1990-2573
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://jeos.ed ... os20220030.html (Publisher URL)
PURE Output Type: Article
Published Date: 2023-01-17
Accepted Date: 2022-12-05
Authors: Donodin, Aleksandr (ORCID Profile 0000-0002-5715-1438)
De Moura, Uiara Celine
Brusin, Ann Margareth Rosa
Manuylovich, Egor (ORCID Profile 0000-0002-5722-0122)
Dvoyrin, Vladislav (ORCID Profile 0000-0001-7358-1641)
Da Ros, Francesco
Carena, Andrea
Forysiak, Wladek (ORCID Profile 0000-0001-5411-1193)
Zibar, Darko
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)



Version: Published Version

License: Creative Commons Attribution

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