Danshi Wang

Simultaneous multichannel wavelength multicasting and XOR logic gate multicasting for three DPSK signals based on four-wave mixing in quantum-dot semiconductor optical amplifier

In this paper, we experimentally demonstrate simultaneous multichannel wavelength multicasting (MWM) and exclusive-OR logic gate multicasting (XOR-LGM) for three 10Gbps non-return-to-zero differential phase-shift-keying (NRZ-DPSK) signals in quantum-dot semiconductor optical amplifier (QD-SOA) by exploiting the four-wave mixing (FWM) process. No additional pump is needed in the scheme. Through the interaction of the input three 10Gbps DPSK signal lights in QD-SOA, each channel is successfully multicasted to three wavelengths (1-to-3 for each), totally 3-to-9 MWM, and at the same time, three-output XOR-LGM is obtained at three different wavelengths. All the new generated channels are with a power penalty less than 1.2dB at a BER of 10(-9). Degenerate and non-degenerate FWM components are fully used in the experiment for data and logic multicasting.

Nonlinearity Mitigation Using a Machine Learning Detector Based on

A powerful machine learning detector based on the k-nearest neighbors (KNN) algorithm is proposed to overcome system impairments. The zero-dispersion link (ZDL), dispersion managed link (DML), and dispersion unmanaged link (DUL) are considered. Meanwhile, an improved algorithm, the distance-weight KNN, is introduced, which outperforms the conventional maximum likelihood-post compensation approach. The numerical results show that KNN is feasible for overcoming various impairments, especially for non-Gaussian symmetric noise, such as laser phase noise and nonlinear phase noise in the ZDL or DML.

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A machine learning-based classifier, namely SVM, is introduced to create the nonlinear decision boundary in M-ary PSK-based coherent optical system to mitigate NLPN. The maximum transmission distance and LPRD tolerance are improved by 480 km and 3.3 dBm for 8PSK.

-Nearest Neighbors

An intelligent constellation diagram analyzer is proposed to implement both modulation format recognition (MFR) and optical signal-to-noise rate (OSNR) estimation by using convolution neural network (CNN)-based deep learning technique. With the ability of feature extraction and self-learning, CNN can process constellation diagram in its raw data form (i.e., pixel points of an image) from the perspective of image processing, without manual intervention nor data statistics. The constellation diagram images of six widely-used modulation formats over a wide OSNR range (15~30 dB and 20~35 dB) are obtained from a constellation diagram generation module in oscilloscope. Both simulation and experiment are conducted. Compared with other 4 traditional machine learning algorithms, CNN achieves the better accuracies and is obviously superior to other methods at the cost of O(n) computation complexity and less than 0.5 s testing time. For OSNR estimation, the high accuracies are obtained at epochs of 200 (95% for 64QAM, and over 99% for other five formats); for MFR, 100% accuracies are achieved even with less training data at lower epochs. The experimental results show that the OSNR estimation errors for all the signals are less than 0.7 dB. Additionally, the effects of multiple factors on CNN performance are comprehensively investigated, including the training data size, image resolution, and network structure. The proposed technique has the potential to be embedded in the test instrument to perform intelligent signal analysis or applied for optical performance monitoring.

Nonlinear decision boundary created by a machine learning-based classifier to mitigate nonlinear phase noise

We propose a multifunctional optical switching unit based on the bidirectional liquid crystal on silicon (LCoS) and semiconductor optical amplifier (SOA) architecture. Add/drop, wavelength conversion, format conversion, and WDM multicast are experimentally demonstrated. Due to the bidirectional characteristic, the LCoS device cannot only multiplex the input signals, but also de-multiplex the converted signals. Dual-channel wavelength conversion and format conversion from 2 × 25Gbps differential quadrature phase-shift-keying (DQPSK) to 2 × 12.5Gbps differential phase-shift-keying (DPSK) based on four-wave mixing (FWM) in SOA is obtained with only one pump. One-to-six WDM multicast of 25Gbps DQPSK signals with two pumps is also achieved. All of the multicast channels are with a power penalty less than 1.1 dB at FEC threshold of 3.8 × 10⁻³.

Intelligent constellation diagram analyzer using convolutional neural network-based deep learning

The capability of multicast directly in the optical domain at the routing nodes is a target for energy saving, fast service provisioning, and effective network resource utilization. An optical network node architecture supporting wavelength conversion, unicast, and multicast is presented and a shared multicast/wavelength conversion module is designed in the intermediate node. Wavelength conversion and one-to-six WDM multicast of 25-Gb/s QPSK signals based on four-wave mixing are experimentally demonstrated using a semiconductor optical amplifier with only two pumps. All six multicast signals achieve error-free performance with a power penalty <;4.3 dB.

Multifunctional switching unit for add/drop, wavelength conversion, format conversion, and WDM multicast based on bidirectional LCoS and SOA-loop architecture

An intelligent eye-diagram analyzer is proposed to implement both modulation format recognition (MFR) and optical signal-to-noise rate (OSNR) estimation by using a convolution neural network (CNN)-based deep learning technique. With the ability of feature extraction and self-learning, CNN can process eye diagram in its raw form (pixel values of an image) from the perspective of image processing, without knowing other eye-diagram parameters or original bit information. The eye diagram images of four commonly-used modulation formats over a wide OSNR range (10~25 dB) are obtained from an eye-diagram generation module in oscilloscope combined with the simulation system. Compared with four other machine learning algorithms (decision tress, k-nearest neighbors, back-propagation artificial neural network, and support vector machine), CNN obtains the higher accuracies. The accuracies of OSNR estimation and MFR both attain 100%. The proposed technique has the potential to be embedded in the test instrument to perform intelligent signal analysis or applied for optical performance monitoring.

Optical Network Node Supporting One-to-Six WDM Multicasting of QPSK Signals

In this paper, we propose a performance monitoring and failure prediction method in optical networks based on machine learning. The primary algorithms of this method are the support vector machine (SVM) and double exponential smoothing (DES). With a focus on risk-aware models in optical networks, the proposed protection plan primarily investigates how to predict the risk of an equipment failure. To the best of our knowledge, this important problem has not yet been fully considered. Experimental results showed that the average prediction accuracy of our method was 95% when predicting the optical equipment failure state. This finding means that our method can forecast an equipment failure risk with high accuracy. Therefore, our proposed DES-SVM method can effectively improve traditional risk-aware models to protect services from possible failures and enhance the optical network stability.

Modulation Format Recognition and OSNR Estimation Using CNN-Based Deep Learning

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Failure prediction using machine learning and time series in optical network

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