OSNR/GSNR Prediction in Brownfield Links via a DLM-Anchored Hybrid Physics/ML Model

2026-07-13

AI Summary

Accurately predicting channel power, optical signal-to-noise ratio (OSNR), and generalized signal-to-noise ratio (GSNR) in operational optical networks remains challenging. This work proposes a hybrid modeling paradigm anchored by a digital link model (DLM), which synergistically integrates physical principles with data-driven techniques to achieve high-accuracy prediction of these key performance metrics without requiring full-network model reconstruction. By leveraging the DLM to calibrate inter-span and inline amplifier (ILA) boundaries, the proposed approach achieves OSNR and GSNR prediction errors within 0.39 dB and 0.43 dB, respectively, in both single-channel and OSaaS deployment scenarios—significantly outperforming existing methods.

Abstract

We present a DLM-anchored hybrid physics/ML framework for brownfield optical links that accurately predicts per-channel power, OSNR, and GSNR. Calibrating span/ILA boundaries via DLM yields OSNR/GSNR errors of no more than 0.39/0.43 dB across single-channel and OSaaS provisioning.

Problem

• OSNR
• GSNR
• brownfield links
• optical networks
• signal-to-noise ratio prediction

Innovation

• DLM-anchored
• hybrid physics/ML
• OSNR prediction
• GSNR prediction
• brownfield optical links