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

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.

Field Tests of AI-Driven Road Deformation Detection Leveraging Ambient Noise over Deployed Fiber Networks

This study demonstrates an AI-driven method for detecting road deformations using Distributed Acoustic Sensing (DAS) over existing telecom fiber networks. Utilizingambient traffic noise, it enables real-time, long-term, and scalable monitoring for road safety.

Field Trials of Manhole Localization and Condition Diagnostics by Using Ambient Noise and Temperature Data with AI in a Real-Time Integrated Fiber Sensing System

Field trials of ambient noise-based automated methods for manhole localization and condition diagnostics using a real-time DAS/DTS integrated system were conducted. Crossreferencingmultiple sensing data resulted in a 94.7% detection rate and enhanced anomaly identification.

High-Sensitivity Forward-Transmission Vibration Sensing for Real-World Event Detection in Urban Fiber Networks

Publication Date: 4/3/2025 Event: OFC 2025 Reference: Th4C.2: 1-3, 2025 Authors: Jian Fang, NEC Laboratories America, Inc.; Ming-Fang Huang, NEC Laboratories America, Inc.; Scott Kotrla, Verizon; Tiejun J. Xia, Verizon; Glenn A. Wellbrock, Verizon; Jeffrey A Mundt, Verizon; Ting Wang, NEC Laboratories America, Inc.; Yoshiaki Aono, NEC Corporation Abstract: Publication Link:

Multi-Event Distributed Forwarding Sensing with Dual-Sensor Adaptive Beamforming

We present adaptive beamforming techniques to forward-transmission multi-event vibration sensing in environments with interference and jamming. Experimental validation over 100km fiber demonstrates significant improvements on signal reconstruction, noise reduction, and interference rejection from other locations.

Multi-span OSNR and GSNR Prediction using Cascaded Learning

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.

Optical Line System Physical Digital Model Calibration using a Differential Algorithm

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.

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

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.

Scalable Machine Learning Models for Optical Transmission System Management

Optical transmission systems require accurate modeling and performance estimation for autonomous adaption and reconfiguration. We present efficient and scalable machine learning (ML) methods for modeling optical networks at component- and network-level with minimizeddata collection.

Statistical Assessment of System Margin in Metro Networks Impaired by PDL

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.