Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

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