GNPy is a tool or software used for Quality-of-Transmission (QoT) estimation in optical networks. It is based on the Generalized Gaussian Noise (GGN) model, designed to predict the signal quality in fiber-optic communication systems. Specifically, GNPy is being enhanced and experimentally validated for C+L multiband transmission (which covers a broad range of frequencies in optical communication).

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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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GNPy as a Benchmark for Open and Disaggregated Optical Networks

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The evolution toward open and partially disaggregated optical networks has introduced new, to our knowledge,requirements on how transmission performance is evaluated and compared across technologies, vendors, and deployment scenarios. In this context, sound benchmarking practices are essential to ensure that quality-of-transmission (QoT) assessments are reproducible, transparent, and meaningful beyond isolated experimental demonstrations. QoT estimation plays a central role in these practices, as it directly impacts network planning,commissioning, automation, and long-term technology selection in heterogeneous optical infrastructures. This paper discusses benchmarking practices for optical transmission in open networks using the open-source GNPy library as a reference digital model. The contribution of this work lies in formalizing how a transparent, vendor-agnostic QoT estimator can be used as a common benchmarking baseline across research and industry. Representative experimental validations spanning short-reach, multiband, and multi-vendor flex-grid transmission scenarios are reviewed and reframed as benchmarking baselines, establishing evidence-based expectations on achievable accuracy and applicability limits under realistic operating conditions. Finally, the paper illustrates how reference QoT models are employed in industry-facing benchmarking workflows,including closed-loop interactions with standardization bodies, multi-vendor planning and automation,procurement processes and strategic network evolution toward emerging architectures.

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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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Multi-span OSNR and GSNR Prediction using Cascaded Learning

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We implement a cascaded learning framework leveraging three different EDFA and fiber component models for OSNR and GSNR prediction, achieving MAEs of 0.20 and 0.14 dBover a 5-span network under dynamic channel loading.

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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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GNPy Experimental Validation in a C+L Multiband Optical Multiplex Section

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The GNPy quality-of-transmission estimator has undergone improvements and rigorous experimental validation in a C+L multiband transmission scenario. This includes the incorporation of a disaggregated generalized Gaussian noise model, along with advanced modeling of amplifiers and transceivers. The recently proposed implementation demonstrates notable enhancements, offering highly accurate GSNR predictions on commercial C+L-band equipment while significantly reducing computation time.

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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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