Semi-supervised Learning Enabled Scalable High-Spatial-Density Channel Multiplexing over Multimode Fibers

Pengfei Fan; Michael Ruddlesden; Yufei Wang; Luming Zhao; Chao Lu; Lei Su

doi:10.23919/OECC/PSC53152.2022.9850043

Semi-supervised Learning Enabled Scalable High-Spatial-Density Channel Multiplexing over Multimode Fibers

Pengfei Fan, Michael Ruddlesden, Yufei Wang, Luming Zhao, Chao Lu, Lei Su^*

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

1 Citation (Scopus)

Abstract

We proposed a semi-supervised confidence-based learning approach (SCALA) to overcome the high-temporal-variability of multimode fiber (MMF) information channels, and experimentally demonstrated continuous transmission of high-spatial-density information with accuracy close to 100% over different MMFs.

Original language	English
Title of host publication	OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9784885523366
DOIs	https://doi.org/10.23919/OECC/PSC53152.2022.9850043
Publication status	Published - 2022
Externally published	Yes
Event	27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022 - Toyama, Japan Duration: 3 Jul 2022 → 6 Jul 2022

Publication series

Name	OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022

Conference

Conference	27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022
Country/Territory	Japan
City	Toyama
Period	3/07/22 → 6/07/22

Keywords

adaptive semi-supervised learning method
deep neural network
fiber optics communications
space-division multiplexing

Access to Document

10.23919/OECC/PSC53152.2022.9850043

Cite this

Fan, P., Ruddlesden, M., Wang, Y., Zhao, L., Lu, C., & Su, L. (2022). Semi-supervised Learning Enabled Scalable High-Spatial-Density Channel Multiplexing over Multimode Fibers. In OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022 (OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/OECC/PSC53152.2022.9850043

Fan, Pengfei ; Ruddlesden, Michael ; Wang, Yufei et al. / Semi-supervised Learning Enabled Scalable High-Spatial-Density Channel Multiplexing over Multimode Fibers. OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022).

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title = "Semi-supervised Learning Enabled Scalable High-Spatial-Density Channel Multiplexing over Multimode Fibers",

abstract = "We proposed a semi-supervised confidence-based learning approach (SCALA) to overcome the high-temporal-variability of multimode fiber (MMF) information channels, and experimentally demonstrated continuous transmission of high-spatial-density information with accuracy close to 100% over different MMFs.",

keywords = "adaptive semi-supervised learning method, deep neural network, fiber optics communications, space-division multiplexing",

author = "Pengfei Fan and Michael Ruddlesden and Yufei Wang and Luming Zhao and Chao Lu and Lei Su",

note = "Publisher Copyright: {\textcopyright} 2022 IEICE.; 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022 ; Conference date: 03-07-2022 Through 06-07-2022",

year = "2022",

doi = "10.23919/OECC/PSC53152.2022.9850043",

language = "English",

series = "OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022",

}

Fan, P, Ruddlesden, M, Wang, Y, Zhao, L, Lu, C & Su, L 2022, Semi-supervised Learning Enabled Scalable High-Spatial-Density Channel Multiplexing over Multimode Fibers. in OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022. OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022, Institute of Electrical and Electronics Engineers Inc., 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022, Toyama, Japan, 3/07/22. https://doi.org/10.23919/OECC/PSC53152.2022.9850043

Semi-supervised Learning Enabled Scalable High-Spatial-Density Channel Multiplexing over Multimode Fibers. / Fan, Pengfei; Ruddlesden, Michael; Wang, Yufei et al.
OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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T1 - Semi-supervised Learning Enabled Scalable High-Spatial-Density Channel Multiplexing over Multimode Fibers

AU - Fan, Pengfei

AU - Ruddlesden, Michael

AU - Wang, Yufei

AU - Zhao, Luming

AU - Lu, Chao

AU - Su, Lei

PY - 2022

Y1 - 2022

N2 - We proposed a semi-supervised confidence-based learning approach (SCALA) to overcome the high-temporal-variability of multimode fiber (MMF) information channels, and experimentally demonstrated continuous transmission of high-spatial-density information with accuracy close to 100% over different MMFs.

AB - We proposed a semi-supervised confidence-based learning approach (SCALA) to overcome the high-temporal-variability of multimode fiber (MMF) information channels, and experimentally demonstrated continuous transmission of high-spatial-density information with accuracy close to 100% over different MMFs.

KW - adaptive semi-supervised learning method

KW - deep neural network

KW - fiber optics communications

KW - space-division multiplexing

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U2 - 10.23919/OECC/PSC53152.2022.9850043

DO - 10.23919/OECC/PSC53152.2022.9850043

M3 - Conference Proceeding

AN - SCOPUS:85137001617

T3 - OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022

BT - OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022

Y2 - 3 July 2022 through 6 July 2022

ER -

Fan P, Ruddlesden M, Wang Y, Zhao L, Lu C, Su L. Semi-supervised Learning Enabled Scalable High-Spatial-Density Channel Multiplexing over Multimode Fibers. In OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (OECC/PSC 2022 - 27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing 2022). doi: 10.23919/OECC/PSC53152.2022.9850043

Semi-supervised Learning Enabled Scalable High-Spatial-Density Channel Multiplexing over Multimode Fibers

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