A Conditional Generative Adversarial Network aided Few-mode Fiber Channel Modeling for large-capacity optical fiber communication

Mengzhu Yuan; Huan Chang; Ming Ma; Ran Gao; Fei Wang; Qi Zhang; Dong Guo; Zhipei Li; Fu Wang; Xin Huang

doi:10.1109/ICOCN59242.2023.10236403

A Conditional Generative Adversarial Network aided Few-mode Fiber Channel Modeling for large-capacity optical fiber communication

Mengzhu Yuan^*, Huan Chang^*, Ming Ma, Ran Gao, Fei Wang, Qi Zhang, Dong Guo, Zhipei Li, Fu Wang, Xin Huang

^*Corresponding author for this work

School of Information and Electronics

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

Abstract

In this paper, a conditional generative adversarial network (CGAN) aided channel modeling technique is proposed for few-mode fiber (FMF) optical communication. Simulation results demonstrate the proposed CGAN-aided FMF modeling technique achieve an attractive effect on modelling accuracy.

Original language	English
Title of host publication	2023 21st International Conference on Optical Communications and Networks, ICOCN 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350343502
DOIs	https://doi.org/10.1109/ICOCN59242.2023.10236403
Publication status	Published - 2023
Event	21st International Conference on Optical Communications and Networks, ICOCN 2023 - Qufu, China Duration: 31 Jul 2023 → 3 Aug 2023

Publication series

Name	2023 21st International Conference on Optical Communications and Networks, ICOCN 2023

Conference

Conference	21st International Conference on Optical Communications and Networks, ICOCN 2023
Country/Territory	China
City	Qufu
Period	31/07/23 → 3/08/23

Keywords

Channel modeling
Conditional generative adversarial network
Few mode fiber

Access to Document

10.1109/ICOCN59242.2023.10236403

Cite this

Yuan, M., Chang, H., Ma, M., Gao, R., Wang, F., Zhang, Q., Guo, D., Li, Z., Wang, F., & Huang, X. (2023). A Conditional Generative Adversarial Network aided Few-mode Fiber Channel Modeling for large-capacity optical fiber communication. In 2023 21st International Conference on Optical Communications and Networks, ICOCN 2023 (2023 21st International Conference on Optical Communications and Networks, ICOCN 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICOCN59242.2023.10236403

Yuan, Mengzhu ; Chang, Huan ; Ma, Ming et al. / A Conditional Generative Adversarial Network aided Few-mode Fiber Channel Modeling for large-capacity optical fiber communication. 2023 21st International Conference on Optical Communications and Networks, ICOCN 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 21st International Conference on Optical Communications and Networks, ICOCN 2023).

@inproceedings{00b1f329e3994eaeb982f7098114a33e,

title = "A Conditional Generative Adversarial Network aided Few-mode Fiber Channel Modeling for large-capacity optical fiber communication",

abstract = "In this paper, a conditional generative adversarial network (CGAN) aided channel modeling technique is proposed for few-mode fiber (FMF) optical communication. Simulation results demonstrate the proposed CGAN-aided FMF modeling technique achieve an attractive effect on modelling accuracy.",

keywords = "Channel modeling, Conditional generative adversarial network, Few mode fiber",

author = "Mengzhu Yuan and Huan Chang and Ming Ma and Ran Gao and Fei Wang and Qi Zhang and Dong Guo and Zhipei Li and Fu Wang and Xin Huang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 21st International Conference on Optical Communications and Networks, ICOCN 2023 ; Conference date: 31-07-2023 Through 03-08-2023",

year = "2023",

doi = "10.1109/ICOCN59242.2023.10236403",

language = "English",

series = "2023 21st International Conference on Optical Communications and Networks, ICOCN 2023",

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

booktitle = "2023 21st International Conference on Optical Communications and Networks, ICOCN 2023",

address = "United States",

}

Yuan, M, Chang, H , Ma, M , Gao, R, Wang, F, Zhang, Q, Guo, D , Li, Z, Wang, F & Huang, X 2023, A Conditional Generative Adversarial Network aided Few-mode Fiber Channel Modeling for large-capacity optical fiber communication. in 2023 21st International Conference on Optical Communications and Networks, ICOCN 2023. 2023 21st International Conference on Optical Communications and Networks, ICOCN 2023, Institute of Electrical and Electronics Engineers Inc., 21st International Conference on Optical Communications and Networks, ICOCN 2023, Qufu, China, 31/07/23. https://doi.org/10.1109/ICOCN59242.2023.10236403

A Conditional Generative Adversarial Network aided Few-mode Fiber Channel Modeling for large-capacity optical fiber communication. / Yuan, Mengzhu; Chang, Huan ; Ma, Ming et al.
2023 21st International Conference on Optical Communications and Networks, ICOCN 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 21st International Conference on Optical Communications and Networks, ICOCN 2023).

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

TY - GEN

T1 - A Conditional Generative Adversarial Network aided Few-mode Fiber Channel Modeling for large-capacity optical fiber communication

AU - Yuan, Mengzhu

AU - Chang, Huan

AU - Ma, Ming

AU - Gao, Ran

AU - Wang, Fei

AU - Zhang, Qi

AU - Guo, Dong

AU - Li, Zhipei

AU - Wang, Fu

AU - Huang, Xin

PY - 2023

Y1 - 2023

N2 - In this paper, a conditional generative adversarial network (CGAN) aided channel modeling technique is proposed for few-mode fiber (FMF) optical communication. Simulation results demonstrate the proposed CGAN-aided FMF modeling technique achieve an attractive effect on modelling accuracy.

AB - In this paper, a conditional generative adversarial network (CGAN) aided channel modeling technique is proposed for few-mode fiber (FMF) optical communication. Simulation results demonstrate the proposed CGAN-aided FMF modeling technique achieve an attractive effect on modelling accuracy.

KW - Channel modeling

KW - Conditional generative adversarial network

KW - Few mode fiber

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U2 - 10.1109/ICOCN59242.2023.10236403

DO - 10.1109/ICOCN59242.2023.10236403

M3 - Conference contribution

AN - SCOPUS:85173077209

T3 - 2023 21st International Conference on Optical Communications and Networks, ICOCN 2023

BT - 2023 21st International Conference on Optical Communications and Networks, ICOCN 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 21st International Conference on Optical Communications and Networks, ICOCN 2023

Y2 - 31 July 2023 through 3 August 2023

ER -

Yuan M, Chang H , Ma M , Gao R, Wang F, Zhang Q et al. A Conditional Generative Adversarial Network aided Few-mode Fiber Channel Modeling for large-capacity optical fiber communication. In 2023 21st International Conference on Optical Communications and Networks, ICOCN 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 21st International Conference on Optical Communications and Networks, ICOCN 2023). doi: 10.1109/ICOCN59242.2023.10236403

A Conditional Generative Adversarial Network aided Few-mode Fiber Channel Modeling for large-capacity optical fiber communication

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