Mitigation of nonlinear phase noise in single-channel coherent 16-QAM systems employing logistic regression - de Paula, Rômulo A. [UNESP]; Marim, Lucas [UNESP]; Penchel, Rafael A. [UNESP]; Bustamante, Yésica R. R.; Abbade, Marcelo L. F. [UNESP]; Perez-Sanchez, Grethell; Aldaya, Ivan [UNESP] (Springer Nature, 2021)
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.
Keywords: Coherent systems; Machine learning; Nonlinearity compensation