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    Long distance distributed fiber optic sensing (DFOS) and WDM communications over repeated fiber spans using all-raman amplification and coding schemes
    1.
    发明授权
    Long distance distributed fiber optic sensing (DFOS) and WDM communications over repeated fiber spans using all-raman amplification and coding schemes 有权

    公开(公告)号：US12206489B2

    公开(公告)日：2025-01-21

    申请号：US18109247

    申请日：2023-02-13

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yue-Kai Huang ,  Ezra Ip

    IPC: H04J14/02

    Abstract: Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously provide DFOS and WDM communications over amplified, multi-span optical WDM optical telecommunications facilities using all Raman amplification and coding schemes. Our all-Raman amplification operates stably—without isolators—and provides sufficient gain to compensate for fiber span loss for both DFOS signals and WDM channel signals—at the same time. Furthermore, our inventive techniques employ signal coding, such as MB-TGD-OFDR for DAS, and we operate our DFOS operation power at a much lower power level as compared to pulse interrogation techniques. With improved OSNR and reduced power using signal coding along with our distributed Raman amplification, our DFOS systems can co-exist with WDM communication channels on the same amplified multi-span fiber optic links over great distances.

    Optical network performance evaluation using a hybrid neural network
    6.
    发明授权
    Optical network performance evaluation using a hybrid neural network 有权

    公开(公告)号：US11133865B2

    公开(公告)日：2021-09-28

    申请号：US17106143

    申请日：2020-11-29

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yue-Kai Huang ,  Shaoliang Zhang ,  Ezra Ip ,  Jiakai Yu

    IPC: H04B10/079 ,  G06N3/04 ,  H04L27/34 ,  H04B10/07

    Abstract: Aspects of the present disclosure describe systems, methods. and structures in which a hybrid neural network combining a CNN and several ANNs are shown useful for predicting G-ONSR for Ps-256QAM raw data in deployed SSMF metro networks with 0.27 dB RMSE. As demonstrated, the CNN classifier is trained with 80.96% testing accuracy to identify channel shaping factor. Several ANN regression models are trained to estimate G-OSNR with 0.2 dB for channels with various constellation shaping. Further aspects include the tuning of existing optical networks and the characterization of retrofit/upgraded optical networks to estimate capacity—both aspects employing our inventive hybrid neural network methodology.

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