Abstract
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.
Highlights
optical performance monitoring (OPM) as well as bit-rate and modulation format identification (BR-MFI) is planned to be an important part of the future optical networks for the purpose of monitoring the quality of optical signals precisely
OPM as well as BR-MFI is planned to be an important part of the future optical networks for the purpose of monitoring the quality of optical signals precisely
The differential group delay (DGD) of the signals are varied in the range of 0-10 ps by using the third component which is the polarization-mode dispersion (PMD) emulator
Summary
OPM as well as BR-MFI is planned to be an important part of the future optical networks for the purpose of monitoring the quality of optical signals precisely. Many proposed OPM techniques presume the prior knowledge of the signal’s format and bit-rate or attain these information from the upper-layer protocols to monitor the network impairments [3]. The associate editor coordinating the review of this article and approving it for publication was Tianhua Xu. develop OPM techniques which can monitor various impairments under different formats and bit-rates without any prior information. BR-MFI is important since the OPM techniques may be signal type dependent. In order to monitor critical optical performance parameters and identify modulation formats/signal rates in a real-time way, it is significant to develop OPM and BR-MFI techniques for the optical network
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