Cost-effective and data size-adaptive OPM at intermediated node using convolutional neural network-based image processor.

Abstract

A cost-effective and data size-adaptive optical performance monitoring (OPM) scheme is proposed, which is based on asynchronous delay-tap plot (ADTP) using convolutional neural network (CNN) from the perspective of image processing. First, we design an OPM framework, based on the electrical domain-processing technique for the future optical networks. These networks include coherent detection-based end-to-end channel monitoring at destination node and direct detection-based transmission link monitoring at intermediate node. Aiming at the link monitoring, CNN is applied to recognize and analyze ADTP images that are converted from two-dimension (2D) digital vectors, so that adaptive to the stable algorithm structure. In simulation system, three high-order modulation formats, 16 quadrature amplitude modulation (QAM), 32QAM, 64QAM, are investigated for optical signal-to-noise ratio (OSNR) estimation and modulation format identification (MFI). The 100% accuracies under different chromatic dispersions (CDs) at different iteration epochs are obtained. Compared with asynchronous amplitude histograms (AAH)-based method, the better accuracy and faster convergence rate are achieved, especially in terms of strong CDs. Additionally, the experimental system is also conducted of 16QAM and 64QAM signals. Based on the partially-trained CNN model from simulation, the OSNR estimation accuracies of 16QAM and 64QAM are 97.81% and 96.56%, respectively. The maximum standard deviation is less than 0.45 dB and the MFI accuracies is 99.84%, presenting the satisfactory results and proving the feasibility of ADTP-based image processor for link monitoring at intermediate nodes.