A simple and accurate semi-analytical model for predicting the gain of a single-stage erbium-doped fiber amplifier embedded with an unknown gain flattening filter is proposed for precise system equalization that is crucial for submarine systems.
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.
A differential algorithm is proposed to calibrate the physical digital model of an optical line system from scratch at the commissioning phase, using minimal measurements and maximizing signal and OSNR estimation accuracy.
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.
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 experimentally demonstrate real time mode division multiplexing free space optical communication with commercial 400G open and disaggregated transponders. As proof of concept,using HG00, HG10, and HG01 modes, we transmit 1.2 Tb/s/l (3´1l´400Gb/s) error free.
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.
The NEC Labs America Optical Networking and Sensing team is attending the 2025 Optical Fiber Communications Conference and Exhibition (OFC), the premier global event for optical networking and communications. Bringing together over 13,500 attendees from 83+ countries, more than 670 exhibitors, and hundreds of sessions featuring industry leaders, OFC 2025 serves as the central hub for innovation and collaboration in the field. At this year’s conference, NEC Labs America will showcase its cutting-edge research and advancements through multiple presentations, demonstrations, and workshops.
In this work, for the first time, we experimentally demonstrate mode division multiplexing-based bidirectional free space optical communication in real-time using commercial transponders. As proof of concept, via bidirectional pairs of Hermite-Gaussian modes (HG00, HG10, and HG01), using a Telecom Infra Project Phoenix compliant commercial 400G transponder, 400-Gb/s data signals (56-Gbaud, DP-16QAM) are bidirectionally transmitted error free, i.e., with less than 1e-2 pre-FEC BERs, over approximately 1-m of free space
