Impairment mitigation in dual-polarization single-span optical digital coherent systems using support vector classifiers

Ivan Aldaya, Lucio Borges, Camila Costa, Julián Pita, Rafael Abrantes Penchel, Jose Augusto de Oliveira, Grethell Georgina Pérez Sánchez

DOI: 10.14209/sbrt.2023.1570915762

Evento: XLI Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2023)

Keywords: Kerr effect Machine learning Optical communications Support vector machines

Abstract

We report on using support vector classifiers (SVCs) to mitigate the residual and fiber-induced nonlinear distortions in a digital coherent optical communication system employing dual-polarization 16-ary quadrature amplitude modulation with a data rate of 100 Gbps. Simulation results reveal that SVC can partially tackle the effect of the intra-polarization and the inter-polarization nonlinear crosstalk. Simulations also show that processing the information of both polarizations leads to improved performance but needs to take special care to avoid overfitting and biasing effect, requiring the implementation of regularization. Regarding the training block size, processing each polarization individually and together require around 20,000 and 27,500 symbols, respectively.

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