Work

Scalable ML models and cascaded learning for efficient multi-span OSNR and GSNR prediction

Year: 2025

Type: article

Source: Journal of Optical Communications and Networking

DOI: 10.1364/jocn.572370

Authors: Zehao Wang, Andrea D’Amico, Giacomo Borraccini, Agastya Raj, Yue-Kai Huang, Shaobo Han, Ting Wang, Marco Ruffini, Dan Kilper, Tingjun Chen

Institutions: NEC (United States), Trinity College Dublin

Language: English

Topic: Optical Network Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: closed

Funders: National Science Foundation, Science Foundation Ireland, HORIZON EUROPE European Innovation Council

Awards: CONNECT_Phase 2, CAREER: Toward Efficient and Scalable Mobile Fronthaul Empowered by Analog Radio-over-Fiber, DubliNets: Distributed laboratory and outdoor testbed infrastructure for research on future end-to-end networks and services for 5G and beyond, EAGER: An Integrated Fiber Sensing and Communication Living Lab in the Research Triangle, IPIC_Phase 2, PAWR Platform COSMOS: Cloud Enhanced Open Software Defined Mobile Wireless Testbed for City-Scale Deployment, TwiLight: Twin Lightpaths, A digital twin framework for full automation of disaggregated optical networks., CC* Integration-Large: Scaling Scientific Workloads on Distributed Commodity GPUs and Storage through Campus-level RDMA Networking, AI Institute for Edge Computing Leveraging Next Generation Networks (Athena), IRNC: Testbed: COSMOS Interconnecting Continents (COSMIC), G8517257691

OpenAlex ID: W4416150254

FWCI: 0.227

Cites: 26

Cited by: 1

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