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.

Automated summary

Bismuth-doped fiber amplifiers are an attractive solution for increasing bandwidth demand in modern communication systems. A new approach using a neural network trained with experimental data sets in E- and S-bands has been proposed to develop a bismuth-doped fiber amplifier model. This approach allows for robust prediction of amplifier operation, considering variations in fiber properties and amplifier characteristics fluctuations. The spectral dependencies of gain and noise figure for given pump currents and input signal powers have been obtained, achieving low mean and standard deviation of error in the 1410-1490 nm spectral band.