Low Complexity OSNR Monitoring and Modulation Format Identification Based on Binarized Neural Networks

Abstract

We propose and experimentally demonstrate a method of optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) using a binarized convolutional neural network (B-CNN) in coherent receiver. The proposed technique automatically extracts OSNR and modulation format dependent features from the signals' ring constellation maps. A group of modulation schemes including nine quadrature amplitude modulation (QAM) formats are selected as transmission signals. The experimental results show that the MFI accuracy can reach 100% and OSNR monitoring accuracy can reach higher than 97.71% for the nine M-QAM modulation formats. Compared with float valued convolutional neural network (F-CNN) and multi-layer perceptron (MLP), B-CNN can reach the same performance in MFI. For OSNR monitoring, the performance of B-CNN is similar to MLP and slightly worse than F-CNN. Moreover, the memory consumption and execution time of B-CNN is much lower than F-CNN and MLP. Therefore, B-CNN is power and time efficient with little performance loss compared with F-CNN and MLP. It is attractive for cost-effective multi-parameter estimation in next-generation optical networks.