Power Evolution Modeling and Optimization of Fiber Optic Communication Systems With EDFA Repeaters

In this article, machine learning is used to create a differentiable model for the input-output power spectral profile relations ofC-band erbium-doped fiber amplifiers (EDFAs). TheEDFA model is demonstrated to generalize to multiple physical devices of the same make while only trained on experimental data from a single unit. The model is combined with a differentiable model for simulating stimulated Raman scattering (SRS) effects during propagation through the the optical fiber to create a differentiable model for a multi-span system with an arbitrary configuration of number of spans, length per span and launch power per span. The cascade system model is used to predict and optimize the power profile output of several such experimental configurations of up to three spans with an arbitrary target power profile. A flat target profile is exemplified experimentally, achieving <3 dB of power excursions for EDFAs exhibiting >10 dB of excursion per device in the cascade. The experimental data used to create the EDFA model is made public and available online.

Automated summary

Machine learning is used to create a differentiable model for the input-output power spectral profile relations of C-band erbium-doped fiber amplifiers (EDFAs). The model is demonstrated to generalize to multiple physical devices of the same make and can be used to predict and optimize the power profile output of multi-span systems with arbitrary configurations.