Abstract
This article considers, for the first time, optical performance monitoring (OPM) in few mode fiber (FMF)-based optical networks. 1-D features vector, extracted by projecting a 2-D asynchronous in-phase quadrature histogram (IQH), and the 2D IQH are proposed to achieve OPM in FMF-based network. Three machine learning algorithms are employed for OPM and their performances are compared. These include support vector machine, random forest algorithm, and convolutional neural network. Extensive simulations are conducted to monitor optical to signal ratio (OSNR), chromatic dispersion (CD), and mode coupling (MC) for dual polarization-quadrature phase shift keying (DP-QPSK) at 10, 12, 16, 20, and 28 Gbaud transmission speeds. Besides, M-ary quadrature amplitude modulation (M = 8 and 16) is considered. Also, the OPM accuracy is investigated under different FMF channel conditions including phase noise and polarization mode dispersion. Simulation results show that the proposed 1D projection features vector provides better OPM results than those of the widely used asynchronous amplitude histogram (AAH) features. Furthermore, it has been found that the 2D IQH features outperform the 1D projection features but require larger number of features samples. Additionally, the effect of fiber nonlinearity on the OPM accuracy is investigated. Finally, OPM using the 2D IQH features has been verified experimentally for 10 Gbaud DP-QPSK signal. The obtained results show a good agreement between both simulation and experimental findings.