IEICE TRANSACTIONS on Communications
Physical Status Representation in Multiple Administrative Optical Networks by Federated Unsupervised Learning
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E106-B No.11 pp.1084-1092
- Publication Date
- 2023/11/01
- Publicized
- 2023/08/01
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2022OBP0004
- Type of Manuscript
- Special Section PAPER (Joint Special Section on Opto-electronics and Communications for Future Optical Network)
- Category
Authors
Takahito TANIMURA
https://orcid.org/0000-0001-5162-1104
Hitachi Ltd.
Riu HIRAI
Hitachi Ltd.
Nobuhiko KIKUCHI
Hitachi Ltd.
Keyword
Latest Issue
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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
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@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},}
Copy
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 -
English
