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.
|Number of pages||6|
|Journal||Journal of the European Optical Society-Rapid Publications|
|Publication status||Published - 17 Jan 2023|
Bibliographical noteFunding 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).
© The Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Doped fiber
- Neural network
- Optical communications
- Optical networks