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 rootmean-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.
We propose a novel feature fusion based multi-task convolutional neural network (ConvNet) for simultaneous bit-rate and modulation format identification (BR-MFI) and optical performance monitoring (OPM) in heterogeneous fiber-optic networks. The proposed multi-task ConvNet fuses the intermediate layers through the convolutional operation and then trains multi-task losses on the fused feature. In addition to traditional multi-task ConvNet's ability of the feature extraction and sharing, our multi-task ConvNet is able to capture both global and local information of phase portraits and has good performance on OPM and BR-MFI tasks in a short processing time (∼51 ms). The simulation results of six signals (consisted of two bit-rates and three modulation formats) demonstrate the root-mean-square (RMS) errors of the optical signal-to-noise ratio (OSNR), chromatic dispersion (CD) and differential group delay (DGD) are 0.81 dB, 1.52 ps/nm and 0.32 ps, respectively. Meanwhile, the 100% classification accuracy can be obtained for BR-MFI. Besides, the effects of the fused feature shape, the location of feature extracted for fusion, the transmitter variations and fiber nonlinearity on the performance of the proposed technique are thoroughly investigated.INDEX TERMS Bit-rate and modulation format identification (BR-MFI), optical performance monitoring (OPM), convolutional neural network (ConvNet), feature fusion.
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