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Artificial Neural Network Symbol Estimator With Enhanced Robustness to Nonlinear Phase Noise

SANTOS, J. ; Carena, A. ; Monteiro, P. ; Guiomar, F. P.

IEEE Photonics Technology Letters Vol. 33, Nº 23, pp. 1341 - 1344, December, 2021.

ISSN (print): 1041-1135
ISSN (online): 1041-1135

Scimago Journal Ranking: 0,81 (in 2020)

Digital Object Identifier: 10.1109/LPT.2021.3120074

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This letter reports a novel approach for nonlinear phase noise mitigation, based on artificial neural networks (ANNs) tailored to classification applications and a pre-processing stage of feature engineering. Starting with a set of proof-ofconcept simulations, we verify that the proposed system can achieve optimal performance for the additive white Gaussian noise (AWGN) channel. Then, considering a dispersion-less channel with strong nonlinear phase noise (NLPN) distortion, we demonstrate a Q-factor increase of 0.4dB, comparing with standard carrier-phase estimation (CPE) followed by minimum distance detection. Finally, simulating the propagation of 64Gbaud PM-16QAM over standard single mode fiber (SSMF), we verify that the ANN-based solution is effective on wavelength-division multiplexing (WDM) transmission conditions, enabling to increase the maximum signal reach by approximately 1 fiber span over the legacy CPE-enabled NLPN compensation.