The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
우리는 광섬유 네트워크의 디지털 응집성 광 수신기가 수신한 신호에서 광 상태를 추출하기 위한 데이터 수집 및 심층 신경망(DNN) 훈련 방식을 제시합니다. DNN은 연합 학습과 비지도 학습을 결합하여 여러 관리 네트워크 도메인에 걸쳐 레이블이 없는 데이터 세트로 훈련됩니다. 이 체계를 통해 네트워크 관리자는 개인 데이터 세트를 공개하지 않고도 네트워크의 광학 상태를 추출하는 일반적인 DNN 기반 인코더를 교육할 수 있습니다. 초기 단계 개념 증명은 64GBd 16QAM 및 직교 위상 편이 키잉 신호를 사용하여 광 신호 대 잡음비 및 변조 형식을 추정하여 시뮬레이션을 통해 수치적으로 입증되었습니다.
Takahito TANIMURA
https://orcid.org/0000-0001-5162-1104
Hitachi Ltd.
Riu HIRAI
Hitachi Ltd.
Nobuhiko KIKUCHI
Hitachi Ltd.
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부
Takahito TANIMURA, Riu HIRAI, Nobuhiko KIKUCHI, "Physical Status Representation in Multiple Administrative Optical Networks by Federated Unsupervised Learning" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 11, pp. 1084-1092, November 2023, doi: 10.1587/transcom.2022OBP0004.
Abstract: We present our data-collection and deep neural network (DNN)-training scheme for extracting the optical status from signals received by digital coherent optical receivers in fiber-optic networks. The DNN is trained with unlabeled datasets across multiple administrative network domains by combining federated learning and unsupervised learning. The scheme allows network administrators to train a common DNN-based encoder that extracts optical status in their networks without revealing their private datasets. An early-stage proof of concept was numerically demonstrated by simulation by estimating the optical signal-to-noise ratio and modulation format with 64-GBd 16QAM and quadrature phase-shift keying signals.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2022OBP0004/_p
부
@ARTICLE{e106-b_11_1084,
author={Takahito TANIMURA, Riu HIRAI, Nobuhiko KIKUCHI, },
journal={IEICE TRANSACTIONS on Communications},
title={Physical Status Representation in Multiple Administrative Optical Networks by Federated Unsupervised Learning},
year={2023},
volume={E106-B},
number={11},
pages={1084-1092},
abstract={We present our data-collection and deep neural network (DNN)-training scheme for extracting the optical status from signals received by digital coherent optical receivers in fiber-optic networks. The DNN is trained with unlabeled datasets across multiple administrative network domains by combining federated learning and unsupervised learning. The scheme allows network administrators to train a common DNN-based encoder that extracts optical status in their networks without revealing their private datasets. An early-stage proof of concept was numerically demonstrated by simulation by estimating the optical signal-to-noise ratio and modulation format with 64-GBd 16QAM and quadrature phase-shift keying signals.},
keywords={},
doi={10.1587/transcom.2022OBP0004},
ISSN={1745-1345},
month={November},}
부
TY - JOUR
TI - Physical Status Representation in Multiple Administrative Optical Networks by Federated Unsupervised Learning
T2 - IEICE TRANSACTIONS on Communications
SP - 1084
EP - 1092
AU - Takahito TANIMURA
AU - Riu HIRAI
AU - Nobuhiko KIKUCHI
PY - 2023
DO - 10.1587/transcom.2022OBP0004
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2023
AB - We present our data-collection and deep neural network (DNN)-training scheme for extracting the optical status from signals received by digital coherent optical receivers in fiber-optic networks. The DNN is trained with unlabeled datasets across multiple administrative network domains by combining federated learning and unsupervised learning. The scheme allows network administrators to train a common DNN-based encoder that extracts optical status in their networks without revealing their private datasets. An early-stage proof of concept was numerically demonstrated by simulation by estimating the optical signal-to-noise ratio and modulation format with 64-GBd 16QAM and quadrature phase-shift keying signals.
ER -