ML-Based Spectral Power Profiles Prediction in Presence of ISRS for Ultra-Wideband Transmission

A generalized method based on machine learning (ML) and artificial neural networks (ANNs) is proposed for a fast and accurate prediction of spectral and spatial evolution of power profiles in support of performance and quality-of-transmission (QoT) real-time assessment of ultra-wideband links. These systems, operating on bandwidths larger than the standard C–band, are affected by inter-channel stimulated Raman scattering (ISRS), whose impact on power profiles evolution along the fiber is generally estimated by solving numerically a set of nonlinear ordinary differential equations (ODEs). However, the computational effort, in terms of complexity and convergence time to the solution, increases with the bandwidth and the number of transmitted wavelength division multiplexing (WDM) channels, which makes the usual approach no longer particularly suitable to operate in real time. To meet the speed requirements, three different ANNs are introduced to make fast predictions of power profiles over frequency and distance considering a wide range of scenarios: different power per channel values, different fiber types and different span lengths. Two ANNs are used on synthetic data to estimate the impact of linear and nonlinear fiber impairments in support of system modeling. Specifically, one to directly predict the evolution of spectral power profiles along the fiber and the other to estimate the coefficients to insert in a closed-form version of the EGN model. A third ANN operates on experimental data and it is used to predict power profiles at the end of the fiber for fast estimations of system performance. The obtained results show highly accurate predictions with values of maximum absolute error, computed between predicted and actual power profiles, not exceeding 0.2 dB for ∼97% of cases for synthetic data and always below 0.5 dB for experimental data. Such results prove the potential of the proposed approach making it suitable for real time application of QoT estimation.