Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication

Vladislav Neskorniuk; Andrea Carnio; Domenico Marsella; Sergei K. Turitsyn; Jaroslaw E. Prilepsky; Vahid Aref

Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication

Vladislav Neskorniuk, Andrea Carnio, Domenico Marsella, Sergei K. Turitsyn, Jaroslaw E. Prilepsky, Vahid Aref

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

Autoencoder-based deep learning is applied to jointly optimize geometric and probabilistic constellation shaping for optical coherent communication. The optimized constellation shaping outperforms the 256 QAM Maxwell-Boltzmann probabilistic distribution with extra 0.05 bits/4D-symbol mutual information for 64 GBd transmission over 170 km SMF link.

Original language	English
Title of host publication	2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings
Publisher	IEEE
Number of pages	2
ISBN (Electronic)	9781957171050
Publication status	Published - 23 Sept 2022
Event	2022 Conference on Lasers and Electro-Optics, CLEO 2022 - San Jose, United States Duration: 15 May 2022 → 20 May 2022 https://www.cleoconference.org/home/schedule/

Publication series

Name	Conference on Lasers and Electro-Optics, CLEO

Conference

Conference	2022 Conference on Lasers and Electro-Optics, CLEO 2022
Country/Territory	United States
City	San Jose
Period	15/05/22 → 20/05/22
Internet address	https://www.cleoconference.org/home/schedule/

Bibliographical note

Funding Information:
Acknowledgements: This project has received funding from EU Horizon 2020 program under the Marie Skłodowska-Curie grant agreement No. 766115 (FONTE). SKT acknowledges the support of EPSRC project TRANSNET.

Access to Document

https://ieeexplore.ieee.org/document/9890753

Cite this

Neskorniuk, V., Carnio, A., Marsella, D., Turitsyn, S. K., Prilepsky, J. E., & Aref, V. (2022). Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication. In 2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings Article SW4E.5n (Conference on Lasers and Electro-Optics, CLEO). IEEE. https://ieeexplore.ieee.org/document/9890753

@inproceedings{a5aea9695a874b9abce807cd90c52c0b,

title = "Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication",

abstract = "Autoencoder-based deep learning is applied to jointly optimize geometric and probabilistic constellation shaping for optical coherent communication. The optimized constellation shaping outperforms the 256 QAM Maxwell-Boltzmann probabilistic distribution with extra 0.05 bits/4D-symbol mutual information for 64 GBd transmission over 170 km SMF link.",

author = "Vladislav Neskorniuk and Andrea Carnio and Domenico Marsella and Turitsyn, {Sergei K.} and Prilepsky, {Jaroslaw E.} and Vahid Aref",

note = "Funding Information: Acknowledgements: This project has received funding from EU Horizon 2020 program under the Marie Sk{\l}odowska-Curie grant agreement No. 766115 (FONTE). SKT acknowledges the support of EPSRC project TRANSNET. ; 2022 Conference on Lasers and Electro-Optics, CLEO 2022 ; Conference date: 15-05-2022 Through 20-05-2022",

year = "2022",

month = sep,

day = "23",

language = "English",

series = "Conference on Lasers and Electro-Optics, CLEO",

publisher = "IEEE",

booktitle = "2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings",

address = "United States",

url = "https://www.cleoconference.org/home/schedule/",

}

Neskorniuk, V, Carnio, A, Marsella, D, Turitsyn, SK , Prilepsky, JE & Aref, V 2022, Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication. in 2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings., SW4E.5n, Conference on Lasers and Electro-Optics, CLEO, IEEE, 2022 Conference on Lasers and Electro-Optics, CLEO 2022, San Jose, United States, 15/05/22. <https://ieeexplore.ieee.org/document/9890753>

Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication. / Neskorniuk, Vladislav; Carnio, Andrea; Marsella, Domenico et al.
2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings. IEEE, 2022. SW4E.5n (Conference on Lasers and Electro-Optics, CLEO).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication

AU - Neskorniuk, Vladislav

AU - Carnio, Andrea

AU - Marsella, Domenico

AU - Turitsyn, Sergei K.

AU - Prilepsky, Jaroslaw E.

AU - Aref, Vahid

N1 - Funding Information: Acknowledgements: This project has received funding from EU Horizon 2020 program under the Marie Skłodowska-Curie grant agreement No. 766115 (FONTE). SKT acknowledges the support of EPSRC project TRANSNET.

PY - 2022/9/23

Y1 - 2022/9/23

N2 - Autoencoder-based deep learning is applied to jointly optimize geometric and probabilistic constellation shaping for optical coherent communication. The optimized constellation shaping outperforms the 256 QAM Maxwell-Boltzmann probabilistic distribution with extra 0.05 bits/4D-symbol mutual information for 64 GBd transmission over 170 km SMF link.

AB - Autoencoder-based deep learning is applied to jointly optimize geometric and probabilistic constellation shaping for optical coherent communication. The optimized constellation shaping outperforms the 256 QAM Maxwell-Boltzmann probabilistic distribution with extra 0.05 bits/4D-symbol mutual information for 64 GBd transmission over 170 km SMF link.

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UR - https://www.cleoconference.org/home/schedule/

M3 - Conference publication

AN - SCOPUS:85139974907

T3 - Conference on Lasers and Electro-Optics, CLEO

BT - 2022 Conference on Lasers and Electro-Optics, CLEO 2022 - Proceedings

PB - IEEE

T2 - 2022 Conference on Lasers and Electro-Optics, CLEO 2022

Y2 - 15 May 2022 through 20 May 2022

ER -

Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Deep Learning Methods for Nonlinearity Mitigation in Coherent Fiber-Optic Communication Links

Cite this

Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Student theses

Deep Learning Methods for Nonlinearity Mitigation in Coherent Fiber-Optic Communication Links

Cite this