Joint nonlinear optical signal-to-noise ratio estimation and modulation format identification based on constellation-points trajectory information and multitask 1DCNN for WDM systems.

Abstract

We propose a joint monitoring scheme of nonlinear optical signal-to-noise ratio (O S N R N L ) estimation and modulation format identification (MFI) in wavelength division multiplexing (WDM) systems. Based on the abundant information of both nonlinear noise (NLN) and modulation format (MF) in received signals, this scheme first counts the trajectory information of all adjacent constellation points, and then quantifies them into the adjacent matrix. Subsequently, the eigenvectors corresponding to the largest eigenvalues are extracted via eigen-decomposition of the adjacent matrix, which characterize the information of NLN and MF effectively. Finally, the eigenvectors are fed into multitask one-dimensional convolutional neural network to perform O S N R N L estimation and MFI simultaneously. To verify the effectiveness of the scheme, five-channel 28 GBaud polarization division multiplexing (PDM) -16/32/64 quadrature amplitude modulation (QAM) WDM simulation systems are built by VPI. The simulation results demonstrate that, for PDM-16/32/64QAM signals, the mean absolute errors of O S N R N L estimation are 0.18, 0.17, and 0.20dB, respectively. At the same time, the identification accuracy rates of these three MFs have achieved 100% within the ranges of estimated O S N R N L . Furthermore, a three-channel 28 GBaud WDM experimental system is constructed to further investigate the effectiveness of trajectory information for O S N R N L estimation. The experimental results show that the O S N R N L estimation errors of PDM-16QAM are less than 0.5dB. In addition, our analysis of complexity from two aspects of trajectory information extraction and neural network model shows that the overall complexity scale of this scheme is O(K i,3 M C i,3 C o,3).