Open AccessLoss weight adaptive multi-task learning based optical performance monitor for multiple parameters estimation
Author(s) -
Zhenming Yu,
Zhiquan Wan,
Liang Shu,
Shaohua Hu,
Yilun Zhao,
Jing Zhang,
Kun Xu
Publication year - 2019
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.037041
Subject(s) - quadrature amplitude modulation , computer science , artificial neural network , modulation (music) , polarization mode dispersion , transmission (telecommunications) , signal to noise ratio (imaging) , multiplexing , optics , mean squared error , adaptive optics , algorithm , bit error rate , optical fiber , artificial intelligence , mathematics , physics , telecommunications , statistics , decoding methods , acoustics
A loss weight adaptive multi-task learning based artificial neural network (MTL-ANN) is applied for joint optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI). We conduct an experiment of polarization division multiplexing (PDM) coherent optical system with 5 km standard single mode fiber (SSMF) transmission to verify this monitor. A group of modulation schemes including nine modulation adaptive M-QAM formats are selected as the transmission signals. Instead of circular constellation, signals' amplitude histograms after constant module algorithm (CMA) based polarization de-multiplexing are selected as input features for our proposed monitor. The experimental results show that the MFI accuracy reaches 100% in the estimated OSNR range. Furthermore, when treated as regression problem and classification problem, OSNR estimation with a root mean-square error (RMSE) of 0.68 dB and an accuracy of 98.7% are achieved, respectively. Unlike loss weight fixed MTL-ANN, loss weight adaptive MTL-ANN could search the optimal loss weight ratio automatically for different link configurations. Besides that, the number of estimated parameters can be easily expanded, which is attractive for multiple parameters estimation in future heterogeneous optical networks.