Learning to Tune OpticalWANs: A Field Deployment of Noise Models in Optical Networks
Accurately modeling optical signal transmission is critical foroptimizing network performance, particularly in large-scalefiber optic networks operated by Internet Service Providers.In this work, we develop a Gaussian Noise model for a NewYork state ISPs optical backbone. Our model accounts for allmajor network components, including amplifiers, fiber spans,reconfigurable optical add-drop multiplexers, and transceivers.By accurately predicting end-to-end signal-to-noise ratio, ourmodel provides a foundation for network performance analysisand optimization. Then, we leverage hyperparameter searchtechniquescommonly used in machine learningto identifyamplifier gain settings that improve signal quality. By treatingthe model as an opaque box, we systematically search foramplifier configurations that maximize the predicted end-to-end SNR while maintaining practical network constraints. Wevalidate our approach through a field deployment by applyingoptimized amplifier gain settings in a live ISP network. Ourresults show a significant improvement in optical signal quality,achieving a 2 dB increase in SNR on a single wavelength 1.
