Optical Performance Monitoring Research Articles

Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

This article considers, for the first time, optical performance monitoring (OPM) in few mode fiber (FMF)-based optical networks. 1-D features vector, extracted by projecting a 2-D 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.

Toward Universal Optical Performance Monitoring for Intelligent Optical Fiber Communication Networks

Optical performance monitoring (OPM) plays an essential role in the intelligent optical fiber communication networks, which perform dynamic network planning and service deployment based on the knowledge of the real-time conditions of the optical layer. Common parameters estimated by OPM include optical power, optical signal-to-noise ratio, chromatic dispersion, polarization mode dispersion, and so on. To be cost-effective and integrated, a universal OPM for multiple parameters realized on a single hardware platform is highly desirable. Although the sources of distinct impairments are independent, the induced distortions on the signal are mixed together, thus challenging the realization of universal OPM. In this article, the existing OPM methods are reviewed with a focus on those based on spectral analysis. A viable solution of universal OPM based on a double-homodyne scheme with all low-cost components is presented.

Transmission-Quality-Aware Online Network Design and Provisioning Enabled by Optical Performance Monitoring

Transmission-Quality-Aware Online Network Design and Provisioning Enabled by Optical Performance Monitoring

A Robust Reference Optical Spectrum Based in-Band OSNR Monitoring Method Suitable for Flexible Optical Networks

The in-band OSNR monitoring methods based on the noise-free reference optical spectrum (ROS) have attracted much attention in the field of optical performance monitoring because they are cost-effective and insensitive to chromatic dispersion and polarization mode dispersion. In this paper, we propose a new ROS based in-band OSNR monitoring method that is robust to the distortions due to non-ideal modulation, the variations of the in-line EDFA gain and the number of filtering elements traversed by the signal. Moreover, this method supports using the standard ROS retrieved by the existing machine-learning technique based optical spectrum identification technique, and thus can remove the need to measure the ROS near the transmitter, which is hard to perform, especially in flexible optical networks. These merits make the method more robust and convenient to use in practice. Extensive numerical simulations are presented to demonstrate the merits of the proposed method with respect to the existing ROS based methods.

A CNN-based cost-effective modulation format identification scheme by low-bandwidth direct detecting and low rate sampling for elastic optical networks

Modulation format identification (MFI) is an indispensable technique in future optical performance monitoring (OPM) for elastic optical networks. The existing MFI scheme based on coherent detection combined with digital signal processing technology has good chromatic dispersion (CD) tolerance, but the high cost prevents its practical application at numerous intermediate nodes. By contrast, the existing direct detection-based MFI has the advantage of low cost but the CD tolerance is poor. In this paper a low-cost direct detection-based MFI scheme is proposed. The scheme not only reduces the cost of the MFI module by low-bandwidth direct detection and low rate sampling, but also ensures high MFI accuracy by extracting and learning features of asynchronous amplitude histograms (AAH) through convolutional neural network (CNN) as it has better accuracy and stability of identification compared with artificial neural network (ANN) and deep neural network (DNN) with the same computational complexity. A maximum correlation coefficient of AAH sequences of different modulation formats is proposed as a metric of discrimination for MFI, and its relationship with the bandwidth of MFI module is investigated in the scenario that the MFI module relative bandwidth changes from 0.00025 to 0.75 of service signal baud rate (SBR) and the CD vary from 0∼16000ps/nm. The simulation and experimental results show that the MFI module with 0.002∼0.02 SBR bandwidth could realize 100% MFI accuracy of three common QAM signals with the CD of 0∼16000ps/nm.

Transfer learning simplified multi-task deep neural network for PDM-64QAM optical performance monitoring.

We experimentally demonstrate a transfer learning (TL) simplified multi-task deep neural network (MT-DNN) for joint optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) from directly detected PDM-64QAM signals. First, we investigate the quality of amplitude histogram (AH) generation on the performance of OSNR monitoring and experimentally clarify the importance of higher electronic sampling rate in order to realize precise OSNR monitoring for high-order QAM format. Next, by implementing TL from simulation to experiment, when both 10Gbaud PDM-16QAM and PDM-64QAM signals are considered, the accuracy of MFI reaches 100% and the root-mean-square error (RMSE) of OSNR monitoring is 1.09dB over a range of 14-24dB and 23-34dB for PDM-16QAM and PDM-64QAM, respectively. Meanwhile, the used training samples and epochs can be substantially reduced by 24.5% and 44.4%, respectively. Since single photodetector (PD) and one TL simplified MT-DNN are used, the proposed optical performance monitoring (OPM) scheme with high cost performance can be applied for advanced modulation formats.

A Density Clustering Algorithm for Simultaneous Modulation Format Identification and OSNR Estimation

In this work, we propose a combined method to implement both modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation, a method based on density-based spatial clustering of applications with a noise (DBSCAN) algorithm. The proposed method can automatically extract the cluster number and density information of samples. The cluster number can be used for MFI and the density information combined with a fourth-order polynomial fitting can correctly estimate OSNR. We verify the feasibility of the method through simulation and conceptual proof experiments. The results show that the MFI can achieve 100% accuracy when the OSNR values are higher than the 7% forward error correction (FEC) thresholds for five commonly used modulation formats (MFs) like polarization division multiplexing (PDM)-QPSK, PDM-8PSK, PDM-16QAM, PDM-32QAM, and PDM-64QAM. Mean absolute OSNR estimation errors are not higher than 1 dB for different signals. There is no additional hardware required, so the proposed method has the ability to be integrated into existing optical performance monitoring systems without burden. Furthermore, the proposed method has the potential to be used in bit-error ratio (BER) calculation, linear, or nonlinear impairments monitoring. We believe that our multifunctional and simple method would be favorable to a future elastic optical network (EON).

Evaluación del desempeño físico de un sistema FTTH-GPON para servicios Quad Play después de la incorporación de un módulo RoF

Este artículo propone el diseño y simulación de un módulo de radio frecuencia basado en un sistema de multiplexación por división de frecuencia ortogonal (OFDM) con modulación de amplitud en cuadratura (16-QAM) a una tasa de transmisión de datos de 10 Gbps y a una frecuencia de 10 GHz, utilizando la herramienta de simulación OptSim. Para ello, se diseñó a nivel de simulación un sistema de red con fibra hasta el hogar (FTTH)-óptica pasiva Gigabit (GPON) Triple Play (servicio de datos/voz a 2,5 Gbps y servicio de video CATV digital con 16-QAM a 0,8 Gbps) con el fin de integrar el módulo de radio sobre fibra (RoF) para ofrecer servicios Quad Play (Triple Play más Internet móvil). Posteriormente, se evaluó el desempeño para el enlace descendente a nivel físico del sistema FTTH-GPON después de que se incorporó el módulo RoF, con base en los parámetros de monitoreo de desempeño óptico (OPM), tales como: potencia de recepción óptica, magnitud del vector de error (EVM), diagrama de constelación, relación señal a ruido (SNR), tasa de error de bit (BER), Factor de calidad (Q) y diagrama del ojo. Los resultados muestran que es posible que los usuarios de las redes FTTH-GPON puedan contar, además de los servicios Triple Play, con un cuarto servicio (Internet móvil) ya que la calidad del servicio móvil está garantizada dentro del “último kilómetro” para redes de acceso, usando el despliegue actual de fibra óptica estándar, demostrando la factibilidad del sistema propuesto.

Emergency OPM Recreation and Telemetry for Disaster Recovery in Optical Networks

Optical performance monitoring (OPM) and the corresponding telemetry systems play an important role in modern optical transport networks based on software-defined networking (SDN). There have been extensive studies and standardization activities to build high-speed and high-accuracy OPM/telemetry systems that can ensure sufficient monitoring data for effective network control and management. However, current solutions for OPM/telemetry assume that control and management planes (C/M-plane) always provide sufficient bandwidth (BW) to deliver telemetry data. Unfortunately, in the event of several concurrent network failures (e.g., following a large-scale disaster), C/M-plane networks can become heavily degraded and/or unstable, and even experience isolation of some of their parts. Under such circumstances, the existing OPM systems would hardly function. To enhance resiliency and to ensure the quick recovery of OPM/telemetry in case of disaster, we propose an approach for quick recreation of OPM and for achieving robust telemetry based on OpenConfig YANG. Our proposal addresses three key problems: (1) how to quickly recreate the lost OPM capability, (2) how to address the mismatch between the high data rate of OPM and the low BW in the C/M-plane network, and (3) how to flexibly reconfigure the telemetry system to be adaptive to sudden BW changes in the C/M-plane network. We implement a testbed and experimentally demonstrate that our proposal can tolerate low post-disaster bandwidth and can adapt the telemetry system following the changing conditions of the C/M-plane network.

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.

Machine Learning Techniques for Optical Performance Monitoring and Modulation Format Identification: A Survey

The trade-off between more user bandwidth and quality of service requirements introduces unprecedented challenges to the next generation smart optical networks. In this regard, the use of optical performance monitoring (OPM) and modulation format identification (MFI) techniques becomes a common need to enable the development of next-generation autonomous optical networks, with ultra-low latency and self-adaptability. Recently, machine learning (ML)-based techniques have emerged as a vital solution to many challenging aspects of OPM and MFI in terms of reliability, quality, and implementation efficiency. This article surveys ML-based OPM and MFI techniques proposed in the literature. First, we address the key advantages of employing ML algorithms in optical networks. Then, we review the main optical impairments and modulation formats being monitored and classified, respectively, using ML algorithms. Additionally, we discuss the current status of optical networks in terms of MFI and OPM. This includes standards, monitoring parameters, and the available commercial products with their limitations. Second, we provide a comprehensive review of the available ML-based techniques for MFI, OPM, and joint MFI/OPM, describing their performance, advantages, and limitations. Third, we give an overview of the exiting ML-based OPM and MFI techniques for the emerging optical networks such as the new fiber-based networks that use future space division multiplexing techniques (e.g., few-mode fiber), the hybrid radio-over-fiber networks, and the free space optical networks. Finally, we discuss the open issues, potential future research directions, and recommendations for the potential implementation of ML-based OPM and MFI techniques. Some lessons learned are presented after each section throughout the paper to help the reader identifying the gaps, weaknesses, and strengths in this field.

Enhancing the Credibility of the Optical Performance Monitor With Adversarial Training

The existing optical performance monitoring (OPM) scheme based on deep neural network has no selection capability of the input data. They always accept and process all, which may result in serious monitoring errors and reduce the credibility of the monitoring system. Because the transmitted data in the future heterogeneous fiber-optic networks are diverse, and it's likely to exceed the scope of the monitoring system. We propose an unsupervised generative adversarial network (GAN) as the judgement module in the new OPM framework to select the legal data within the scope of the monitoring system. The generator consists of encoder-decoder-encoder (EDE) sub-network, jointly learns the image and latent feature distribution of the legal data. And the training data for the network in the new added judgement module is the same as the OPM analyzer network's, therefore, no extra data are collected, which is low-cost. In the simulation, four modulation formats under two bit-rates are taken into account to verify the model performance in the judgement module. When 60 Gbps 64QAM signal is selected as illegal data, the max value of the area under the curve (AUC) is 0.942. The judgement time for single image is about 12 ms. Moreover, the influence of the task weights and the latent feature shape on the judgement performance are investigated. The new added judgement module largely increases the credibility and safety of the existing OPM scheme.

Reliable Optical Performance Monitor: The Combination of Parallel Framework and Skip Connected Generative Adversarial 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 light-weight 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.

Heuristic Chaotic Hurricane-Aided Efficient Power Assignment for Elastic Optical Network

In this paper, we propose a dynamical power allocation (PA) procedure for elastic optical networks (EONs) based on the evolutionary hurricane search optimization (HSO) algorithm with a chaotic logistic map diversification strategy with the purpose of improving the capability to escape from local optima, namely PA-CHSO. The aiming is the dynamical control of the transmitted optical powers according to the variations of each link state due to traffic fluctuations, channel impairments, as well as other channel-power coupling effects. Such realistic EON scenarios are affected mainly by the channel estimation inaccuracy, channel ageing and power fluctuations. The link state is based on the channel estimation and quality of transmission (QoT) parameters obtained from the optical performance monitors (OPMs). Numerical results have demonstrated the effectiveness of the PA-CHSO to dynamically mitigate the power penalty under real measurement conditions with uncertainties and noise, as well as when perturbations in the optical transmit powers are considered.

Loss weight adaptive multi-task learning based optical performance monitor for multiple parameters estimation.

A loss weight adaptive multi-task learning based artificial neural network (MTL-ANN) is applied for joint optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI). We conduct an experiment of polarization division multiplexing (PDM) coherent optical system with 5 km standard single mode fiber (SSMF) transmission to verify this monitor. A group of modulation schemes including nine modulation adaptive M-QAM formats are selected as the transmission signals. Instead of circular constellation, signals' amplitude histograms after constant module algorithm (CMA) based polarization de-multiplexing are selected as input features for our proposed monitor. The experimental results show that the MFI accuracy reaches 100% in the estimated OSNR range. Furthermore, when treated as regression problem and classification problem, OSNR estimation with a root mean-square error (RMSE) of 0.68 dB and an accuracy of 98.7% are achieved, respectively. Unlike loss weight fixed MTL-ANN, loss weight adaptive MTL-ANN could search the optimal loss weight ratio automatically for different link configurations. Besides that, the number of estimated parameters can be easily expanded, which is attractive for multiple parameters estimation in future heterogeneous optical networks.

On-chip Spectrometer Formed by a Multi-stage Structure

With apparent size and weight advantages, on-chip spectrometer could be a good choice for the spectrum analysis application which has been widely used in numerous areas such as optical network performance monitoring, materials analysis and medical research. In order to realize the broadband and the high resolution simultaneously, we propose a new on-chip spectrometer structure, which is a two-stage structure. The coarse wavelength division is realized by the cascaded Mach-Zehnder interferometers, which is the first stage of the spectrometer. The output of the Mach-Zehnder interferometers are further dispersed by the second stage structure, which can be realized either by arrayed waveguide gratings or by digital Fourier transform spectrometer structure. We further implemented the thermo-optic modulation for the arrayed waveguide gratings to achieve a higher spectral resolution. The output channel wavelengths of the spectrometer are modulated by the embedded heater to obtain the first order derivative spectra of the input optical signal to obtain a 2nm resolution. With respect to the computer simulation and device characterization results, the 400nm spectral range and the nanoscale resolution have been demonstrated.

Monitoring of OSNR Using an Improved Binary Particle Swarm Optimization and Deep Neural Network in Coherent Optical Systems

A novel technique is proposed to implement optical signal-to-noise ratio (OSNR) estimation by using an improved binary particle swarm optimization (IBPSO) and deep neural network (DNN) based on amplitude histograms (AHs) of signals obtained after constant modulus algorithm (CMA) equalization in an optical coherent system. For existing OSNR estimation models of DNN and AHs, sparse AHs with valid features of original data are selected by IBPSO algorithm to replace the original, and the sparse sets are used as input vector to train and test the particle swarm optimization (PSO) optimized DNN (PSO-DNN) network structure. Numerical simulations have been carried out in the OSNR ranges from 10 dB to 30 dB for 112 Gbps PM-RZ-QPSK and 112 Gbps PM-NRZ-16QAM signals, and results show that the proposed algorithm achieves a high OSNR estimation accuracy with the maximum estimation error is less than 0.5 dB. In addition, the simulation results with different data input into the deep neural network structure show that the mean OSNR estimation error is 0.29 dB and 0.39 dB under original data and 0.29 dB and 0.37 dB under sparse data for the two signals, respectively. In the future dynamic optical network, it is of more practical significance to reconstruct the original signal and analyze the data using sparse observation information in the face of multiple impairment and serious interference. The proposed technique has the potential to be applied for optical performance monitoring (OPM) and is helpful for better management of optical networks.

Eye Diagram Measurement-Based Joint Modulation Format, OSNR, ROF, and Skew Monitoring of Coherent Channel Using Deep Learning

In this work, deep learning is used to monitor coherent channel performance with eye diagram measurement. Experiments show that the proposed technique can determine the modulation format, optical signal to noise ratio (OSNR), roll-off factor (ROF), and timing skew of a quadrature amplitude modulation (QAM) transmitter with high accuracy. Trained vanilla convolutional neural network (CNN) and MobileNet can be utilized to jointly monitor above four parameters with >98% prediction accuracy for 32 GBd coherent channels with quadrature phase shift keying (QPSK), 8-QAM or 16-QAM formats, under OSNR from 15 to 40 dB, IQ skew from −15 to 15 ps, ROF from 0.05 to 1. Our proposed deep learning approach outperforms many traditional machine learning methods, such as decision tree, k -nearest neighbor algorithm (KNN), and histogram of oriented gradient (HOG) based support vector machine (SVM). Unlike other optical performance monitoring approaches, the use of eye diagram measurement combined with deep learning could enable joint monitoring of multiple system performance parameters with reduced hardware implementation complexity. Comparing with vanilla CNN, MobileNet has relatively simplified iteration algorithm, thus reduces the requirement on the computational power, while still maintaining high accuracy for classification issues.

Multi-task deep neural network (MT-DNN) enabled optical performance monitoring from directly detected PDM-QAM signals.

We experimentally demonstrate a multi-task deep neutral network (MT-DNN) enabled optical performance monitoring (OPM) for PDM-QPSK/8QAM/16QAM signals, by using the asynchronous amplitude histogram (AAH) after the direct detection. Consequently, we can simultaneously realize the modulation format identification (MFI), baud rate identification (BRI), and optical signal-to-noise ratio (OSNR) monitoring simultaneously. In the simulation, when both 20Gbaud and 30Gbaud PDM-QPSK, PDM-8QAM and PDM-16QAM signals are taken into account, the accuracies of both MFI and BRI are 100%. Meanwhile, the root-mean-square error (RMSE) of OSNR monitoring is 0.58dB over a range of 10-22dB, 14-24dB, and 17-26dB for PDM-QPSK, PDM-8QAM and PDM-16QAM, respectively. Furthermore, the numerical results show that RMSE of OSNR monitoring is degraded from 0.58dB to 0.97dB, when chromatic dispersion (CD) is accumulated from 0 to 1600ps/nm. Meanwhile, the MFI accuracy is degraded from 100% to 97.25%, and the BRI accuracy remains 100%. When 2.8Gbaud and 9.8Gbaud signals are used for the experimental verification under the condition of back-to-back transmission, the accuracies of MFI and BRI are 100% and 96.8%, respectively, and the RMSE of OSNR monitoring is 0.76dB. Since only one photodetector (PD) with the asynchronous sampling and one MT-DNN are required, the proposed OPM scheme has the advantage of high cost performance.

Experimental Study of Machine-Learning-Based Detection and Identification of Physical-Layer Attacks in Optical Networks

Optical networks are critical infrastructure supporting vital services and are vulnerable to different types of malicious attacks targeting service disruption at the optical layer. Due to the various attack techniques causing diverse physical-layer effects, as well as the limitations and sparse placement of optical performance monitoring devices, such attacks are difficult to detect, and their signatures are unknown. This paper presents an experimental investigation of a machine learning (ML) framework for detection and identification of physical-layer attacks, based on experimental attack traces from an operator field deployed testbed with coherent receivers. We perform in-band and out-of-band jamming signal insertion attacks, as well as polarization scrambling attacks, each with varying intensities. We then evaluate eight different ML classifiers in terms of their accuracy, and scalability in processing experimental data. The optical parameters critical for accurate attack identification are identified and the generalization of the models is validated. Results indicate that artificial neural networks achieve 99.9% accuracy in attack type and intensity classification, and are capable of processing 1 million samples in less than 10 seconds.