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

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...

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Bibliographic Details
Published in2023 23rd International Conference on Transparent Optical Networks (ICTON) pp. 1 - 4
Main Authors Saif, Waddah S., Kumar Orappanpara Soman, Sunish, Dobre, Octavia A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 02.07.2023
Subjects
Amplitude modulation
Backpropagation
Machine learning
Multiplexing
nonlinearity compensation
Optical fiber networks
Q-factor
subcarrier multiplexing
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Summary: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.
ISSN:2161-2064
DOI:10.1109/ICTON59386.2023.10207533