Vittorio Curri works at Politecnico di Torino.
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.
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.
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.
We experimentally justify the need of analyzing stochastic PDL insertion inboptical metro network nodes. Consequently, we assess conservative OSNR margin comparingdifferent approaches to the case with maxwellian-distributed PDL, through Monte Carlo simulation.
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.
We combine few-shot learning and grey-box modeling for EDFAs in optical lines, training a single EDFA model on 500 spectral loads and transferring it to other EDFAs using 4-8 samples, maintaining low OSNR prediction error.
We demonstrate the use of existing terrestrial optical networks as a smart sensing grid, employing a bidirectional long short-term memory (Bi-LSTM) model enhanced with an attention mechanism to detect road vehicles. The main idea of our approach is to deploy a fast, accurate and reliable trained deep learning model in each network element that is constantly monitoring the state of polarization (SOP) of data signals traveling through the optical line system (OLS). Consequently, this deployment approach enables the creation of a sensing smart grid that can continuously monitor wide areas and respond with notifications/alerts for road traffic situations. The model is trained on the synthetic dataset and tested on the real dataset obtained from the deployed metropolitan fiber cable in the city of Turin. Our model is able to achieve 99% accuracy for both synthetic and real datasets.
We report the first field verification of fault localization in an optical line system (OLS) by integrating digital longitudinal monitoring and OLS calibration, highlighting changes in physical metrics and parameters. Use cases shown are degradation of a fiber span loss and optical amplifier noise figure.
In Optical Multiplex Section (OMS) control and optimization framework, end-to-end (Global) and span-by-span (Local) DNN gray-box strategies are compared in terms of scalability and accuracy of the output signal and noise power predictions. Experimental measurements are carried out in OMSs with increasing number of spans.
The noise figure ripple of a dual-stage EDFA is studied starting from experimental measurements under full spectral load conditions and defining device characteristics. Asemi-analytical model is then proposed showing 0.1 dB standard deviation on the error distribution in all cases of operation.
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