Joint Optical Performance Monitoring and Modulation Format/Bit-Rate Identification by CNN-Based Multi-Task Learning

Abstract

A novel technique is proposed for joint multi-impairment optical performance monitoring (OPM) with bit-rate and modulation format identification (BR-MFI) in next-generation heterogeneous optic communication networks by convolution neural network (CNN)-based deep multi-task learning (MTL) on asynchronous delay-tap sampling phase portraits. Instead of treating the monitoring and identification tasks as separate problems, a novel MTL technique is used to joint optimization of them utilizing the ability of feature extraction and feature sharing. Compared with principal component analysis-based pattern recognition algorithm, CNN-based MTL achieves the better accuracies and has a shorter processing time (∼56 ms). The combination signals of three modulation formats and two bit rates under various impairments are used in numerical simulation. For OPM, the results show monitoring of optical signal-to-noise ratio, chromatic dispersion, and differential group delay with root-mean-square error of 0.73 dB, 1.34 ps/nm, and 0.47 ps, respectively. Similarly, for BR-MFI, even in the case of limited training data, 100% accuracies can be achieved. Additionally, the effects of training data size, task weights, and model structure on CNN-based MTL performance are comprehensively studied. The proposed technique can intelligently analyze the signals of future heterogeneous optic communication networks, and the analysis results are helpful for better management of optical networks.