Low Complexity OSNR Monitoring and Modulation Format Identification Based on Binarized Neural Networks
Yilun Zhao, Zhenming Yu, Zhiquan Wan, Shaohua Hu, Liang Shu, Jing Zhang, Kun Xu
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.