KerrNet: Fast and Accurate Estimation of Nonlinear Interference in Elastic Meshed Optical Networks Leveraging Artificial Intelligence

Due to the constant demand for higher capacity in optical networks, the need for accurate and fast Quality of Transmission (QoT) estimators in optical meshed networks is growing. Techniques based on machine learning and artificial intelligence have proven to be efficient for developing such QoT tools in recent years. This work aims to extend our previously developed Machine learning (ML)-based framework called KerrNet Ye et al. 2022, Ye et al. 2023, Ye et al. 2025, which rapidly and reliably estimates the variance of nonlinear interference (NLI) distortions in heterogeneous multi-span coherent WDM transmission systems. We expand KerrNet’s capabilities by using a modified version of the theoretical time-domain regular perturbation (TDRP) model to address elastic WDM scenarios with variable symbol-rate per WDM channel; we also add support for negative dispersions in the fibers, allowing for estimation of fully or partially dispersion-managed networks. Moreover, we introduce improved pre-processing and dataset generation methods, to simplify the design and construction of our ML-based solution, and present extensive numerical validations of the TDRP model.