Andrea D’Amico Andrea D’Amico is a Researcher in the Optical Networking and Sensing Department at NEC Laboratories America. He holds a Ph.D. in Telecommunications Engineering from Politecnico di Torino, where his research focused on quality of transmission estimation, nonlinear fiber modeling, and the application of machine learning to open optical networks. He also earned a Master’s degree in Theoretical Physics and a Bachelor’s degree in Physics from the Università di Pisa.

At NEC, Andrea conducts advanced research for the integration of physical-layer modeling and system-level intelligence to support the evolution of large-scale optical networks. His work focuses on abstracting complex signal behaviors into models that inform automation, optimization, and control across diverse deployment scenarios. By aligning physical accuracy with network adaptability, he contributes to the development of flexible and high-capacity infrastructures capable of meeting the demands of next-generation connectivity.

Posts

Resilient DFOS Placement Strategy for Power Grid Monitoring: Integrating Fiber and Power Network Dependencies

/in /by

We propose a novel Distributed Fiber Optic Sensing (DFOS) placement strategy tailored to the evolving needs of modern power grids, where fiber cables serve dual purposes: communication and real-time sensing. Our approach integrates a heuristic algorithm, PURE (Power Source-aware Route Exploration), with Integer Linear Programming (ILP) to optimize DFOS placement while addressing power supply constraints. The strategy ensures resilient monitoring across diverse grid scenarios by prioritizing observability during outages and leveraging advancements in fiber infrastructure deployment. Case studies demonstrate the effectiveness of our methodology in maintaining power grid resilience while minimizing deployment costs.

Read more

Variable Temperature and Pump Power Semi-Analytical Gain Model for GFF-Embedded Single-Stage EDFAs

/in /by

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.

Read more

Statistical Assessment of System Margin in Metro Networks Impaired by PDL

/in /by

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.

Read more

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

/in /by

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.

Read more

Optical Line System Physical Digital Model Calibration using a Differential Algorithm

/in /by

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.

Read more

Multi-span OSNR and GSNR Prediction using Cascaded Learning

/in /by

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.

Read more

Enhancing EDFAs Greybox Modeling in Optical Multiplex Sections Using Few-Shot Learning

/in /by

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.

Read more

1.2 Tb/s/l Real Time Mode Division Multiplexing Free Space Optical Communication with Commercial 400G Open and Disaggregated Transponders

/in /by

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.

Read more

A Smart Sensing Grid for Road Traffic Detection Using Terrestrial Optical Networks and Attention-Enhanced Bi-LSTM

/in /by

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.

Read more

NEC Labs America Attended OFC 2025 in San Francisco

/in /by

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.

Read more