Intelligent optical performance monitor using multi-task learning based artificial neural network

Future optical network is developing towards highly heterogeneity and flexibility, which means that the various signals will be transmitted in the network and the optical performance monitor is more likely to encounter the signal beyond its monitoring range. When the signal beyond monitoring range (abnormal data) is input, the conventional optical performance monitoring (OPM) framework without the ability of data filtering will produce completely wrong results. Although the serial OPM framework has the ability of data filtering, it increases the processing time cost. We propose a novel parallel OPM framework, in which the judgement and analysis modules process the input data simultaneously to reduce the time cost. Moreover, the lightweight and high-performance skip connected generative adversarial network (GAN) trained only on the normal data (within the monitoring range) is proposed in the judgement module to filter the abnormal data in a fast-speed way (~9 ms). In the simulation, eight common signals are used to test the performance of the skip connected GAN in the judgement module. The optimal area under the curve (AUC) value of 0.952 is obtained when the abnormal data is defined as 60 Gbps 64QAM signal. Besides, the impact of the latent vector length, the task weights, the weight of abnormal score, shifted K values and training data size on the model performance are studied. INDEX TERMS generative adversarial network (GAN), optical performance monitoring (OPM).

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“…where   RI  is the reconstruction loss between the generated and input images as stated in equation (6).…”

Section: The Skip Connected Gan In Judgement Modulementioning

confidence: 99%

Reliable Optical Performance Monitor: The Combination of Parallel Framework and Skip Connected Generative Adversarial Network

et al. 2020

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“…Although this technique provides high accuracy results, it requires precise time clock recovery. The authors in [33] proposed simultaneous MFI and OSNR monitoring using amplitude histogram with multitask learning based artificial neural network. This approach is advantageous since it does not need timing recovery and the associated hardware.…”

Section: Introductionmentioning

confidence: 99%

Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Saif

Ragheb

Alshawi

et al. 2021

J. Lightwave Technol.

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This paper considers, for the first time, optical performance monitoring (OPM) in few mode fiber (FMF)-based optical networks. One dimensional (1D) features vector, extracted by projecting a two-dimensional (2D) asynchronous in-phase quadrature histogram (IQH), and the 2D IQH are proposed to achieve OPM in FMF-based network. Three machine learning algorithms are employed for OPM and their performances are compared. These include support vector machine, random forest algorithm, and convolutional neural network. Extensive simulations are conducted to monitor optical to signal ratio (OSNR), chromatic dispersion (CD), and mode coupling (MC) for dual polarization-quadrature phase shift keying (DP-QPSK) at 10, 12, 16, 20, and 28 Gbaud transmission speeds. Besides, M-ary quadrature amplitude modulation (M = 8 and 16) is considered. Also, the OPM accuracy is investigated under different FMF channel conditions including phase noise and polarization mode dispersion. Simulation results show that the proposed 1D projection features vector provides better OPM results than those of the widely used asynchronous amplitude histogram (AAH) features. Furthermore, it has been found that the 2D IQH features outperform the 1D projection features but require larger number of features samples. Additionally, the effect of fiber nonlinearity on the OPM accuracy is investigated. Finally, OPM using the 2D IQH features has been verified experimentally for 10 Gbaud DP-QPSK signal. The obtained results show a good agreement between both simulation and experimental findings.

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“…Modulation format is also a key parameter to be recognized since it is related to the optimized digital signal processing (DSP) in coherent receiver and elastic bandwidth access for optical network [5,6]. Recently, there is growing interest in deep learning (DL) which has been demonstrated its feasibility to realize different parameters' monitoring and overcome the bottleneck of monitoring when different impairments are physically inseparable in traditional OPM [7][8][9][10]. Most of the neural networks are used separately to monitor the modulation format or the OSNR, respectively.…”

Section: Introductionmentioning

confidence: 99%

Joint Modulation Format Identification and OSNR Monitoring Using Cascaded Neural Network With Transfer Learning

Zhang

et al. 2021

IEEE Photonics J.

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We propose a cascaded neural network (NN) to simultaneously identify the modulation formats and monitor the optical-signal-to-noise ratio (OSNR). In the second-level network, it is a single deep NN (DNN) rather than multiple sub-networks, which makes the architecture more compact and can save the resource for real implementation. However, since the data set is constituted from all modulation formats, the universality can be guaranteed but not for the accuracy and the complexity. To accelerate the estimation process and improve the accuracy, we introduce the transfer learning (TL) and reconstruct the data set with a part from all of the modulation formats for universality and another part from a specific modulation format for TL to pursue higher accuracy. In the experiment, we compare the proposed cascaded single neural network (CSNN) with or without TL, cascaded multiple neural networks (CMNN) and adaptive multi-task learning (MTL) for MFI and OSNR monitoring. In the first-level NN, all of the three schemes can achieve the accuracy of MFI as 100 %. In the second-level NN, the CSNN with TL (TL-CSNN) can significantly improve the training speed and decline the RMSE of 0.19 dB compared with CSNN without TL. The TL-CSNN also has faster convergence speed and is more stable compared with CMNN and adaptive MTL.

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Intelligent optical performance monitor using multi-task learning based artificial neural network

Cited by 63 publications

References 27 publications

Reliable Optical Performance Monitor: The Combination of Parallel Framework and Skip Connected Generative Adversarial Network

Reliable Optical Performance Monitor: The Combination of Parallel Framework and Skip Connected Generative Adversarial Network

Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Joint Modulation Format Identification and OSNR Monitoring Using Cascaded Neural Network With Transfer Learning

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