Toward Intelligent and Efficient Optical Networks: Performance Modeling, Co-existence, and Field Trials

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Publication Date: 7/1/2025

Event: OECC/PSC 2025

Reference: ThG1-2: 1-3, 2025

Authors: Zehao Wang, Duke University, NEC Laboratories America, Inc.; Agastya Raj, Trinity College; Yue-Kai Huang, NEC Laboratories America, Inc.; Ezra Ip, NEC Laboratories America, Inc.; Giacomo Borraccini, NEC Laboratories America, Inc.; Andrea D’Amico, NEC Laboratories America, Inc.; Shaobo Han, NEC Laboratories America, Inc.; Zhenzhou Qi, Duke University,; Gil Zussman, Columbia University; Koji Asahi, NEC Corporation; Hideo Kageshima, NEC Corporation; Yoshiaki Aono, NEC Corporation; Ting Wang, NEC Laboratories America, Inc.; Marco Ruffini, Trinity College; Dan Kilper, Trinity College; Tingjun Chen, Duke University

Abstract: Optical transmission networks require intelligent traffic adaptation and efficient spectrum usage. We present scalable machine learning (ML) methods for network performance modeling, and field trials of distributed fiber sensing and classic optical network traffic coexistence.

Publication Link: https://ieeexplore.ieee.org/document/11110008