Mitigation of nonlinear phase noise in single-channel coherent 16-QAM systems employing logistic regression

• Autor(a) principal: de Paula, Rômulo A. [UNESP]
• Data de Publicação: 2021
• Outros Autores: Marim, Lucas [UNESP], Penchel, Rafael A. [UNESP], Bustamante, Yésica R. R., Abbade, Marcelo L. F. [UNESP], Perez-Sanchez, Grethell, Aldaya, Ivan [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1007/s11082-021-03149-7; http://hdl.handle.net/11449/222197
• topic: Coherent systems; Machine learning; Nonlinearity compensation

We propose and analyze a classifier based on logistic regression (LR) to mitigate the impact of nonlinear phase noise (NPN) caused by Kerr-induced self-phase-modulation in digital coherent systems with single-channel unrepeated links. Simulation results reveal that the proposed approach reduces the bit error ratio (BER) in a 100-km-long 16 quadrature amplitude modulation (16-QAM) system operating at 56-Gbps. Thus, the BER is reduced from 6.88 × 10−4 when using maximum likelihood to 4.27 × 10−4 after applying the LR-based classification, representing an increase of 0.36 dB in the effective Q-factor. This performance enhancement is achieved with only 624 operations per symbol, which can be easily parallelized into 16 lines of 39 operations.