Mitigation of nonlinear phase noise in coherent 16-QAM long-reach PONs by K-nearest neighbors-based classification

Nonlinear phase-noise induced by the Kerr effect is the main nonlinear impairment in single-channel coherent long-reach passive optical networks (LR-PONs). In this work, we explore the capability of the K-nearest neighbors (KNN) algorithm to mitigate this impairment in links with non-negligible fiber dispersion. Simulation results show that when employing KNN in a 56-Gbps coherent LR-PON with a 100-km range and 1:64 splitting ratio, the effective Q-factor is improved by 0.15 dB with respect to maximum likelihood. This increment is achieved by setting the parameter K to 13, which leads to a minimum training data set size of 500 symbols.