A Machine Learning-Based Approach for Nonlinearity Compensation in Subcarrier Multiplexing System

Waddah S. Saif; Sunish Kumar Orappanpara Soman; Octavia A. Dobre

doi:10.1109/icton59386.2023.10207533

A Machine Learning-Based Approach for Nonlinearity Compensation in Subcarrier Multiplexing System

Waddah S. Saif, Sunish Kumar Orappanpara Soman, Octavia A. Dobre

Faculty Of Computing, Eng. & Built Env.

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

Abstract

In this paper, we investigate the potential of applying machine learning (ML) technique for nonlinearity compensation in subcarrier multiplexing (SCM) systems. We propose an extension of the learned digital backpropagation (LDBP) method, which was originally developed for single carrier systems, to effectively deal with the effects of both self-subcarrier and cross-subcarrier nonlinearities in a 64-quadrature amplitude modulation dual-polarization system with 32 Gbaud transmission. The performance of our proposed approach is evaluated and compared with non-ML approaches in the literature. The outcomes demonstrate that our approach outperforms the SCM-DBP method by 0.3 dB. The results of this study demonstrate the potential of using ML techniques to improve the transmission performance of SCM systems which also encourages the further exploration of ML techniques for nonlinearity compensation in SCM systems.

Original language	English
Title of host publication	2023 23rd International Conference on Transparent Optical Networks (ICTON)
Publisher	IEEE
Pages	1-4
Number of pages	4
ISBN (Electronic)	979-8-3503-0303-2
ISBN (Print)	979-8-3503-0304-9
DOIs	https://doi.org/10.1109/icton59386.2023.10207533
Publication status	Published online - 8 Aug 2023
Event	2023 23rd International Conference on Transparent Optical Networks : ICTON - Bucharest, Romania Duration: 2 Jul 2023 → 6 Jul 2023 https://www.gov.pl/web/instytut-lacznosci/icton-2023

Publication series

Name	2023 23rd International Conference on Transparent Optical Networks (ICTON)
Publisher	IEEE Control Society

Conference

Conference	2023 23rd International Conference on Transparent Optical Networks
Abbreviated title	ICTON
Country/Territory	Romania
City	Bucharest
Period	2/07/23 → 6/07/23
Internet address	https://www.gov.pl/web/instytut-lacznosci/icton-2023

Bibliographical note

Funding Information:
This work has been supported by Huawei Technologies Canada.

Publisher Copyright:
© 2023 IEEE.

Keywords

machine learning
nonlinearity compensation
subcarrier multiplexing

Access to Document

10.1109/icton59386.2023.10207533

Cite this

Saif, W. S., Kumar Orappanpara Soman, S., & Dobre, O. A. (2023). A Machine Learning-Based Approach for Nonlinearity Compensation in Subcarrier Multiplexing System. In 2023 23rd International Conference on Transparent Optical Networks (ICTON) (pp. 1-4). (2023 23rd International Conference on Transparent Optical Networks (ICTON)). IEEE. Advance online publication. https://doi.org/10.1109/icton59386.2023.10207533

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title = "A Machine Learning-Based Approach for Nonlinearity Compensation in Subcarrier Multiplexing System",

abstract = "In this paper, we investigate the potential of applying machine learning (ML) technique for nonlinearity compensation in subcarrier multiplexing (SCM) systems. We propose an extension of the learned digital backpropagation (LDBP) method, which was originally developed for single carrier systems, to effectively deal with the effects of both self-subcarrier and cross-subcarrier nonlinearities in a 64-quadrature amplitude modulation dual-polarization system with 32 Gbaud transmission. The performance of our proposed approach is evaluated and compared with non-ML approaches in the literature. The outcomes demonstrate that our approach outperforms the SCM-DBP method by 0.3 dB. The results of this study demonstrate the potential of using ML techniques to improve the transmission performance of SCM systems which also encourages the further exploration of ML techniques for nonlinearity compensation in SCM systems.",

keywords = "machine learning, nonlinearity compensation, subcarrier multiplexing",

author = "Saif, {Waddah S.} and {Kumar Orappanpara Soman}, Sunish and Dobre, {Octavia A.}",

note = "Funding Information: This work has been supported by Huawei Technologies Canada. Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 23rd International Conference on Transparent Optical Networks : ICTON, ICTON ; Conference date: 02-07-2023 Through 06-07-2023",

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Saif, WS, Kumar Orappanpara Soman, S & Dobre, OA 2023, A Machine Learning-Based Approach for Nonlinearity Compensation in Subcarrier Multiplexing System. in 2023 23rd International Conference on Transparent Optical Networks (ICTON). 2023 23rd International Conference on Transparent Optical Networks (ICTON), IEEE, pp. 1-4, 2023 23rd International Conference on Transparent Optical Networks , Bucharest, Romania, 2/07/23. https://doi.org/10.1109/icton59386.2023.10207533

A Machine Learning-Based Approach for Nonlinearity Compensation in Subcarrier Multiplexing System. / Saif, Waddah S.; Kumar Orappanpara Soman, Sunish; Dobre, Octavia A.
2023 23rd International Conference on Transparent Optical Networks (ICTON). IEEE, 2023. p. 1-4 (2023 23rd International Conference on Transparent Optical Networks (ICTON)).

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

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AU - Dobre, Octavia A.

PY - 2023/8/8

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N2 - In this paper, we investigate the potential of applying machine learning (ML) technique for nonlinearity compensation in subcarrier multiplexing (SCM) systems. We propose an extension of the learned digital backpropagation (LDBP) method, which was originally developed for single carrier systems, to effectively deal with the effects of both self-subcarrier and cross-subcarrier nonlinearities in a 64-quadrature amplitude modulation dual-polarization system with 32 Gbaud transmission. The performance of our proposed approach is evaluated and compared with non-ML approaches in the literature. The outcomes demonstrate that our approach outperforms the SCM-DBP method by 0.3 dB. The results of this study demonstrate the potential of using ML techniques to improve the transmission performance of SCM systems which also encourages the further exploration of ML techniques for nonlinearity compensation in SCM systems.

AB - In this paper, we investigate the potential of applying machine learning (ML) technique for nonlinearity compensation in subcarrier multiplexing (SCM) systems. We propose an extension of the learned digital backpropagation (LDBP) method, which was originally developed for single carrier systems, to effectively deal with the effects of both self-subcarrier and cross-subcarrier nonlinearities in a 64-quadrature amplitude modulation dual-polarization system with 32 Gbaud transmission. The performance of our proposed approach is evaluated and compared with non-ML approaches in the literature. The outcomes demonstrate that our approach outperforms the SCM-DBP method by 0.3 dB. The results of this study demonstrate the potential of using ML techniques to improve the transmission performance of SCM systems which also encourages the further exploration of ML techniques for nonlinearity compensation in SCM systems.

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Saif WS, Kumar Orappanpara Soman S, Dobre OA. A Machine Learning-Based Approach for Nonlinearity Compensation in Subcarrier Multiplexing System. In 2023 23rd International Conference on Transparent Optical Networks (ICTON). IEEE. 2023. p. 1-4. (2023 23rd International Conference on Transparent Optical Networks (ICTON)). Epub 2023 Aug 8. doi: 10.1109/icton59386.2023.10207533

A Machine Learning-Based Approach for Nonlinearity Compensation in Subcarrier Multiplexing System

Abstract

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Keywords

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