Network-Wide QoT Estimation With Optimized Gradient Transfer Between Wavelengths

In optical networks, assessing the quality of transmission (QoT) of future demands is critical for optimized network operation. Given the recent advances in network telemetry and Big Data processing, there has been a growing trend to leverage the telemetry of existing connections to enhance the QoT estimation for future demands. Currently, QoT estimation techniques are categorized into three classes: physics-informed analytical models, machine learning (ML) models, and hybrid models, combining the first two classes. Hybrid models have the potential to achieve accurate QoT estimation with reasonable explainability. In this work, we propose a hybrid QoT estimation technique based on gradient transfer between wavelengths using a simplified network digital twin. The proposed approach relies on the GN model to create a starting point NDT (greenfield), which is then updated with signal-to-noise ratio (SNR) telemetry from deployed transponders (brownfield) using gradient descent techniques. By assuming that neighboring channels have similar performance, the method enables learning from adjacent lightpaths. We use well-established ML optimizers to enhance the convergence rate and reduce steady-state estimation error. Compared with classical analytical models, simulation results indicate improved accuracy with reasonable explainability in network scenarios with imperfect amplifier parameters.