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Learning to Tune Optical WANs: A Field Deployment of Noise Models in Optical Networks#

NSDI '26 digest Program Core pick 4–6 May 2026

Abstract Paper Slides Recording

Bhaskar Kataria, Howard Hua (Cornell), Andrea D'Amico (NEC Labs), Bill Owens (NYSERNet), Rachee Singh (Cornell)

Play the recording: Learning to Tune Optical WANs: A Field Deployment of Noise Models in Optical Networks

  • Why: Rare example of physical-layer optimization (amplifier gain tuning) actually deployed in a live ISP backbone with measured results — the kind of ground-truth that usually stays inside vendor tooling.
  • What: A Gaussian Noise model of a New York state ISP's optical backbone covering amplifiers, fiber spans, ROADM s, and transceivers; hyperparameter search over this model identifies amplifier gain settings that improve end-to-end SNR. In a live field deployment, the tuned settings achieved a 2 dB SNR improvement on a single wavelength.
  • How: Model the full optical chain to predict end-to-end SNR accurately, then treat the model as an opaque box and run ML-style hyperparameter search over amplifier configurations under practical network constraints.
  • Where applicable: Small/mid ISP optical backbones (NYSERNet-scale); the 2 dB single-wavelength result is modest but real. Techniques are broadly reusable for any operator that wants model-driven optical tuning instead of vendor-blessed defaults; not a hyperscale DWDM result.