A Digital Twin is a virtual model that accurately simulates and represents the physical characteristics and behaviors of a partially disaggregated optical network. By creating this digital representation, the control architecture can predict how changes in the network affect its operation and enable the system to autonomously adjust the working points of amplifiers. This capability enhances the network’s resilience by mitigating soft failures, thereby ensuring consistent and reliable performance.

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Exact PDL-Induced SNR Statistics in Dual-Polarization Coherent Optical Communication Systems

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We derive the exact probability density function of the signal to noise ratio (SNR) impairment caused by polarization dependent loss (PDL) in coherent optical systems. Using thehinge model for PDL statistics, we construct a sequence of variable transformations that yields the expression of the SNR probability density function by means of a sequence of nested recursive integrals. Monte Carlo simulations validate the analytical formulation assuming realistic PDL levels and link configurations, showing excellent agreement. The model accurately captures SNR variability introduced by cascaded PDL elements and provides a foundation for statistical margin assessment, digital twin modeling, and quality of transmission estimation in flexible, low marginoptical networks.

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Closed-Form Statistical Modeling of PDL-Induced SNR Margins for Reliable Optical Networks

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We develop closed-form formulas for PDL-induced SNR margins using solutions based on central limit theorem. Experimental validations confirm accurate and conservative performancepredictions, enabling precise quality of transmission assessment and margin-aware design in optical networks.

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

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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 ISP’s 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 searchtechniques—commonly used in machine learning—to 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.

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Agnostic QoT Probing via Receiver-Side ASE Loading in a Production Metro for Transparent Datacenter Exchange

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We demonstrate agnostic QoT probing for datacenter exchange in a metro network via receiver-side ASE loading. Knowing BER telemetry and the progressive ASEload, the device estimates GSNR, enabling IPoWDM operations and digital-twin calibration.

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Digital Twins Beyond C-band Using GNPy

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GNPy advancements enable accurate and efficient modeling of multiband optical networks for digital twin applications. The developed solvers for Kerr nonlinearity and SRS have been validated through simulation and experimentally in C+L transmission, supporting real-world network planning, design, and performance optimization across disaggregated optical infrastructures.

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QoT Digital Twin for Bridging Physical Layer Knowledge Gaps in Multi-Domain Networks

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We propose building a spectrally resolved QoT Digital Twin for optical network domains where models and telemetry are unavailable, by probing transmission on a singlespectral slot, using GNPy, and demonstrating accurate experimental results.

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Optical Amplified Line Self-Healing Using GNPy as a Service by the SDN Control

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A control architecture for a partially disaggregated optical network is proposed using a GNPy-based digital twin for QoT estimation. The proposed implementation enables soft failure mitigation by autonomously adjusting the amplifier working points.

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