Optical Performance Monitoring Research Articles

DSP-Based Link Tomography for Amplifier Gain Estimation and Anomaly Detection in C+L-Band Systems

A successful migration from current C-band based optical networks to a multiband scenario primarily depends on the development of solutions that can reliably measure physical properties of optical links over broad spectral transmission windows. Additionally, these solutions must be capable of delivering wavelength-dependent and spatially-resolved indicators that can empower network operators to identify faults before they lead to severe service disruptions. Recently, the exploitation of receiver based digital signal processing as a tool for optical performance monitoring has gained tremendous popularity. One successful example is the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> power profile estimator, which can reconstruct the per-channel longitudinal power profile along the optical fiber link solely processing the received signal samples. In this work, we propose a novel application for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> power profile estimator by harnessing it on multiple wavelengths to accurately estimate the spectral gain profile of C+L-band in-line Erbium-doped fiber amplifiers deployed in a 280-km single mode fiber link. Furthermore, we show how this scheme can be efficiently used to detect amplification-related anomalies, such as gain tilt and narrowband gain compression. In our measurements, we achieved a sub-dB estimation accuracy by comparing the proposed gain extraction approach with the back-to-back characterization obtained from an optical spectrum analyzer.

Intelligent equally weighted multi-task learning for joint OSNR monitoring and modulation format identification

A novel scheme for combining modulation format identification (MFI) and optical performance monitoring (OPM) in elastic optical networks using convolutional-neural-network-based and equally weighted multi-task learning is proposed. In this scheme, shared features of optical signal-to-noise ratio (OSNR) and modulation formats (10 GBaud QPSK, QAM8, QAM16, QAM64) are extracted from constellation maps and used for multi-label classification. Simulation results demonstrate that the accuracies of MFI and OSNR can reach 100% stably and continuously. Moreover, we evaluated the robustness of this model in a system with fixed physical parameters and concluded that it is tolerant to chromatic dispersion and transmission distance. This technology predicts an image in ∼0.51 ms. Compared with the general multi-task learning (MTL) scheme, the proposed approach massively saves execution time (∼107 h) by removing weight selection. It is time-efficient and intelligent for OPM equipment that will perform multiple monitoring tasks in next-generation optical networks.

Progresses of Pilot Tone Based Optical Performance Monitoring in Coherent Systems

Pilot tone (PT) is a low frequency, small intensity modulation applied to high speed optical channel. Traditionally PT is used for channel identification and channel power monitoring. A pilot tone scheme suitable for optical performance monitoring (OPM) in coherent optical communication systems is described. Two effects, Stimulated Raman scattering (SRS) and dispersion fading, are discussed. An important enhancement, multiband pilot tone, is introduced, which provides sub-channel monitoring capability. With the advanced PT technologies, signal spectrum, and relative frequency offset between signal and optical filter can be monitored with sub-GHz resolution. PT also enables direct OSNR and fiber nonlinear noise monitoring with high accuracy and sensitivity.

Machine Learning-Based Optical Performance Monitoring for Super-Channel Optical Networks

In this paper, and for the first time in literature, optical performance monitoring (OPM) of super-channel optical networks is considered. In particular, we propose a novel machine learning OPM technique based on the use of transformed in-phase quadrature histogram (IQH) features and support vector regressor (SVR) to estimate different optical parameters such as optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD). Two transformation methods, the two-dimensional (2D) discrete Fourier transform (DFT) and 2D discrete cosine transform (DCT), are applied to the IQH to extract features with a considerably reduced dimensionality. For the purpose of simulation, the OPM of a 7 × 20 Gbaud dual-polarization–quadrature phase shift keying (DP-QPSK) is considered. Simulations reveal that it can accurately estimate the various optical parameters (i.e., OSNR and CD) with a coefficient of determination value greater than 0.98. In addition, the effectiveness of proposed OPM scheme is examined under different values of polarization mode dispersion and frequency offset, as well as the utilization of different higher order modulation formats. Moreover, proof-of-concept experiments are performed for validation. The results show an excellent matching between the simulation and experimental findings.

Vector distribution measurement of PMD in optical fiber links employing a wavelength-tunable SOP-OTDR.

A measurement of polarization mode dispersion (PMD) vector distribution is implemented with a wavelength-tunable state-of-polarization-detection-based optical time domain reflectometry (SOP-OTDR). Derived from the dynamic equation between the PMD vector and the birefringence vector with a piecewise approximation method, we present an equation for piecewise expression of the relation between the two vectors based on the approximation that the second-order partial derivative of the PMD vector with respect to the length is negligible in each short-enough segment of optical fiber. Utilizing the birefringence vector distributions at three adjacent wavelengths, both the magnitude and the direction distributions of the PMD vector have been calculated through the numerical solution algorithm. The calculation results indicate that the measured magnitudes of PMD vectors are consistent with the statistical experience, which is the Maxwell probability distribution, and the second-order partial derivative magnitudes of the PMD vectors conform to the lognormal distribution. This method could provide a distributed approach for optical performance monitoring by PMD-related characteristics in optical fiber links.

Transfer learning approach toward joint monitoring of bit rate and modulation format.

Convolutional neural network based transfer learning (TL) is proposed to achieve joint optical performance monitoring with bit rate and modulation format identification in optical communication systems. TL is used to improve the execution of various tasks by extracting features without knowing other optical link parameters. Eye diagrams of four different modulation formats are generated at optical signal-to-noise ratios (OSNRs) varying from 15 to 30 dB for two distinct bit rates, which are then identified simultaneously with a trained deep neural network. In addition, comparisons of different TL approaches are presented. The database is divided into distinct categories with varying parameter ranges in offline mode, and prediction models are assigned to each class. The results suggest that the proposed system may greatly increase identification performance over existing strategies by utilizing TL techniques. The impacts of training, testing, and validation data size, as well as model structure based on TL, are also thoroughly investigated. The results reveal that the VGG16 achieves the highest accuracies compared to other deep learning algorithms even at low OSNR values of 20 dB. The proposed structure can intelligently evaluate the signals of future heterogeneous optical communications, and the results can be used to enhance optical network management.

Optical performance monitoring in transparent fiber-optic networks using neural networks and asynchronous amplitude histograms

Recently, technologies such as artificial neural networks (ANNs) and asynchronous amplitude histograms (AAHs) are widely employed in the optical performance monitoring (OPM). In this paper, the number of layers and the optimization of the neural networks are investigated to complete a series of monitoring tasks in optical communication systems with a large range of chromatic dispersion (CD) and optical signal-to-noise ratio (OSNR). It is shown that the monitoring accuracy has been significantly improved with such implementations. Numerical simulations, which have been conducted for six different modulation formats, demonstrate a good monitoring performance, with an average OSNR monitoring error of 0.1064 dB (99.49% monitoring accuracy) for the OSNR range of 10–40 dB and an average CD monitoring error of 3.3324 ps/nm (99.45% monitoring accuracy) for the CD range of 170–1870 ps/nm, respectively. Furthermore, the CD monitoring and the modulation format identification (MFI) have also been investigated for the system with a large range of dispersion. An average CD error of 16.6832 ps/nm (99.45% monitoring accuracy) and a 100% MFI accuracy have been achieved for all six modulation formats. This scheme is also verified experimentally with OSNR errors of 0.41 dB and CD errors of 15.21 ps/nm, respectively. The identification accuracy of the three modulation formats in the experimental system is also 100%.

Narrow-band tunable optical filters based on cascaded Fabry-Perot cavities.

Narrow-band tunable optical filters (TOF) based on three cascaded Fabry-Perot (FP) cavities are demonstrated. The FP cavities are fabricated with Pb(Mg1/3Nb2/3)O3-PbTiO3 (PMN-PT), which is a transparent electro-optic ceramic. With a reflective design, the optical signal passes the three FP cavities twice. Thus we can design the FP cavities with a relatively low reflectivity, which enables it more tolerant to the loss in the cavities. Two types of TOF samples are fabricated. The specifications of the transmissive type TOF with dual-ports are tuning range >48nm, FWHM (full width half maximum) <0.06nm, insertion loss <3.87dB, crosstalk <-39dB, which meet the requirements by optical performance monitor (OPM) applications. The specifications of the reflective type TOF with single-port are tuning range >48nm, FWHM (full width half maximum) <0.1nm, insertion loss <2.82dB, crosstalk <-30dB, which can be employed in tunable fiber lasers for wavelength selection and tuning.

Multi-Task Learning Convolutional Neural Network and Optical Spectrums Enabled Optical Performance Monitoring

We propose a simultaneous optical signal recognition (OSR) and optical signal-to-noise ratio (OSNR) monitoring method by using multi-task learning convolutional neural network (MTL-CNN) in conjunction with optical spectrums, which enables optical performance monitoring (OPM) in the optical transmission network. In order to achieve a trade-off between monitoring loss and time consumption of the MTL-CNN constructed for seven commonly used signals with a spectrum resolution of 10 pm, the number of feature map channels in four convolutional layers is delicately designed as 32, 32, 64, and 64 with task weights set to 0.4 and 0.2, corresponding to OSNR monitoring and OSR, respectively. Simulation results manifest that this method can realize the recognition of the received optical signals with an overall accuracy of 100% and also enable OSNR monitoring with the mean absolute error (MAE) of 0.262 dB. The proposed method shows strong robustness to various distortions and exhibits good performance in terms of time consumption. The effectiveness is further verified by proof-of-concept experiments in three signals. These illustrate that our method is a promising solution for multi-parameter monitoring with high accuracy and efficiency.

Optical signal monitoring using multi-task residual network

Optical signal monitoring, including optical performance monitoring (OPM) and bit rate and modulation format identification (BR-MFI), has attracted considerable interest owing to the advantages in automatic system diagnosis and the establishment of adaptive optical networks. However, the lack of effective multi-parameter shared feature extraction methods makes the optical signal monitoring still elusive and hinders its practical application. Exploiting shortcut connections that allow features to propagate across multiple layers to effectively reduce the low-level feature loss, we introduce a multi-task residual network (MT-ResNet) to extract optical signal feature information from eye diagrams. The results show that the model exhibits a multi-parameter shared feature extraction ability and OPM and BR-MFI can be directly realized. The average root-mean-square errors of optical signal-to-noise ratio, chromatic dispersion, and differential group delay monitoring are 0.84 dB, 1.8 ps/nm, and 0.18 ps, respectively. The accuracies of BR-MFI reach 100%. We demonstrate that the model is robust by investigating its feature extraction ability in the case of increased fiber nonlinearity and untrained samples, and the universality of the model is proved by applying it to a long-reach optical coherent communication system. Our simulation results indicate that this MT-ResNet can be deployed to analyze the signal quality cost-effectively, with a high potential for future dynamic and heterogeneous optical networks.

Simultaneous baud rate/modulation format identification and multi-parameter optical performance monitoring using multi-task learning with enhanced picture of Radon transform

We propose a simultaneous baud rate identification (BRI), modulation format identification (MFI) and multi-parameter optical performance monitoring (OPM) scheme for elastic optical networks (EONs). Firstly, based on the advantage that enhanced picture of Radon transform (EPRT) contains more phase, amplitude, and density information of signals, the proposed scheme takes EPRT, which is obtained from the original constellation diagram of EON signals after pretreatment of grayscale processing, edge detection and Radon transform, as the key feature for signal parameter identification and monitoring. Subsequently, the EPRTs are fed into a multi-task learning (MTL) neural network to perform BRI, MFI, chromatic dispersion identification (CDI), differential group delay (DGD) monitoring and optical signal-to-noise ratio (OSNR) estimation simultaneously. The numerical results of 14/28 GBaud polarization division multiplexing (PDM)-EON transmission system demonstrate that the accuracies of BRI, MFI and CDI could achieve almost 100% after 105 epochs, 114 epochs and 174 epochs respectively. For the task of OSNR estimation, its mean absolute errors (MAE) converges to 0.468 dB after 187 epochs, and the MAE of DGD monitoring is stable at 0.021 times symbol period after 170 epochs. Furthermore, the effectiveness of the proposed scheme is further verified by 14/28 GBaud PDM-EON proof-of-concept experimental system. The results manifest that the MFI and BRI accuracy achieve almost 100% after 118 epochs and 175 epochs, respectively. For the OSNR estimation task, its MAE converges to 0.542 dB after 200 epochs.

Joint multi-parameter optical performance monitoring scheme based on trajectory information for a Stokes vector direct detection system.

In this study, we propose and verify a joint multi-parameter optical performance monitoring (OPM) scheme based on trajectory information for the Stokes vector direct detection (SVDD) system, for the first time, to the best of our knowledge. Here, the proposed scheme first performs quantification of the trajectory to construct trajectory information, which not only presents diversity of the received symbols in spatial dimension, but also records the jump pattern among symbols in time dimension. Subsequently, eigenanalysis is introduced to extract critical features hidden in trajectory information and simultaneously achieve the purpose of dimensionality reduction. The effectiveness of the scheme is verified through 14/28 GBaud SVDD binary phase shift keying/quadrature phase shift keying/-8 quadrature amplitude modulation (QAM)/-16QAM/-32QAM/-64QAM simulation systems. Under the scenario of joint modulation format (MF) identification and optical signal to noise ratio (OSNR) monitoring, the identification rates of all six kinds of MFs achieve 100% within their corresponding reasonable OSNR ranges. Besides that, the average mean absolute error (MAE) of the monitored OSNRs are obtained as 0.03 dB, 0.22 dB, 0.36 dB, 0.41 dB, 0.46 dB, and 0.49 dB for those six kinds of MFs, respectively. Under the scenario of multi-parameter OPM, SVDD-8QAM/-16QAM/-32QAM signals are 100% successfully identified when residual chromatic dispersion (RCD) is located in the ranges of 0-200 ps/nm, 0-190 ps/nm, and 0-160 ps/nm, respectively. The average MAE of OSNR monitoring and RCD estimation for these three commonly used MFs are 1.08 dB and 3.23 ps/nm, respectively. Moreover, the study also demonstrates the robustness for baud rates and a relatively simpler calculation complexity about the proposed OPM scheme.

Optical performance monitoring using SOI-based spectral analysis.

A novel optical performance monitoring (OPM) method based on Fourier transform spectrum analysis (FTSA) is designed for optical signal-to-noise ratio (OSNR) monitoring, modulation format and baud rate recognition in the presence of fiber nonlinearities. The interference intensities, which reflect spectral features of signals, are obtained by exploiting the FTSA consisting of two-stage Mach-Zehnder interferometer (MZI) arrays. Then, the mapping between the OPM parameters and modulated interference intensity (MII) is characterized using neural networks without prior knowledge of the configuration of the communication network. Results show that optical performance parameters are monitored simultaneously. Meanwhile, the accuracy of modulation format and baud rate recognition is 94.8% and most (over 86%) OSNR monitoring errors are less than ±1 dB under complex transmission conditions in presence of frequency offset and delay jitter. Besides, the FTSA can be fabricated on a silicon on insulator (SOI) platform with a large fabrication tolerance, and it has broad working bandwidth to support the full optical communication band. Therefore, the proposed OPM method is capable of integration and miniaturization, which can be ubiquitously applied in network intermediate nodes to support the construction of smart optical networks.

Machine Learning Based Low-Cost Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Real-time optical performance monitoring (OPM) is of the utmost importance in adaptive optical networks to enable awareness of channel conditions and to achieve high quality of service. In single-mode fiber (SMF)-based networks, optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD) monitoring have been extensively studied in the literature. In this work, we consider OPM in few-mode fiber (FMF) networks employing non-coherent detection. OPM in such networks is a challenging task, as FMF has an additional performance-limiting impairment over SMF, namely mode coupling (MC). Here, we propose an OPM scheme to estimate three FMF channel parameters: OSNR within the range of 8 to 20 dB, CD within the range of 160 to 1120 ps/nm, and different levels of MC. The proposed scheme uses a stacked auto-encoder (AE) to extract features with reduced dimensionality compared to the original data. These features are used to train an artificial neural network (ANN) regressor. Simulation results show that the proposed OPM scheme can accurately estimate the OSNR, CD, and MC with root mean square error (RMSE) values of 0.0015 dB, 0.28 ps/nm, and 7.88 × 10−6, respectively. The performance of proposed OPM scheme is also evaluated against different types of features commonly used in literature.

Modulation format identification using the Calinski-Harabasz index.

Modulation format identification (MFI) is a key technology in optical performance monitoring for the next-generation optical network, such as the intelligent cognitive optical network. An MFI scheme based on the Calinski-Harabasz index for a polarization-division multiplexing (PDM) optical fiber communication system is proposed. The numerical simulations were carried out on a 28 Gbaud PDM communication system. The results show that the required minimum optical signal-to-noise ratio values of each modulation format to achieve 100% identification accuracy are all equal to or lower than their corresponding 7% forward error correction thresholds, and the proposed scheme is robust to residual chromatic dispersion. Meanwhile, the proposed scheme was further verified by 20 Gbaud PDM-QPSK/16QAM/32QAM long-haul fiber transmission experiments. The results show that the scheme has a good reliability when fiber non-linear impairments exist. In addition, the complexity of the scheme is significantly lower than that of other clustering-based MFI schemes.

Optical performance monitoring via domain adversarial adaptation in few-mode fiber

We propose and demonstrate an optical performance monitoring (OPM) scheme in few-mode fiber (FMF) based elastic optical networks (EONs). In this work, an adversarial transfer learning (TL) assisted by deep neural networks (DNN), which is used to learn the feature of signal constellations, is investigated for modulation format recognition (MFR) and optical signal-to-noise (OSNR) estimation. Because mode interference affects the projected results in FMF, the DNN is trained to increase link damage tolerance through domain adversarial adaptation (DAA). Five modulation formats have been considered for verifying the feasibility of the proposed scheme, including BPSK, QPSK, 8PSK, 8QAM, and 16QAM. The symbol rate of 12.5 Gbaud is experimentally tested for each scheme across a FMF transmission system. The results reveal that DAA scheme outperforms retraining DNN for requiring one-third training data without any accuracy penalty. Furthermore, the root mean square error (RMSE) of OSNR estimation in this paper can achieve less than 0.1 dB. We anticipate that our results can stimulate further research on the OPM tasks with a feasible deep learning scheme and contribute to the development of FMF-based EONs.

Machine Learning Applications for Short Reach Optical Communication

With the rapid development of optical communication systems, more advanced techniques conventionally used in long-haul transmissions have gradually entered systems covering shorter distances below 100 km, where higher-speed connections are required in various applications, such as the optical access networks, inter- and intra-data center interconnects, mobile fronthaul, and in-building and indoor communications. One of the techniques that has attracted intensive interests in short-reach optical communications is machine learning (ML). Due to its robust problem-solving, decision-making, and pattern recognition capabilities, ML techniques have become an essential solution for many challenging aspects. In particular, taking advantage of their high accuracy, adaptability, and implementation efficiency, ML has been widely studied in short-reach optical communications for optical performance monitoring (OPM), modulation format identification (MFI), signal processing and in-building/indoor optical wireless communications. Compared with long-reach communications, the ML techniques used in short-reach communications have more stringent complexity and cost requirements, and also need to be more sensitive. In this paper, a comprehensive review of various ML methods and their applications in short-reach optical communications are presented and discussed, focusing on existing and potential advantages, limitations and prospective trends.

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

Future communication systems are faced with increased demand for high capacity, dynamic bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have become more complex and thus require new design methods and monitoring techniques, as they evolve towards becoming autonomous. Machine learning has come to the forefront in recent years as a promising technology to aid in this evolution. Optical fiber communications can already provide the high capacity required for most applications, however, there is a need for increased scalability and adaptability to changing user demands and link conditions. Accurate performance monitoring is an integral part of this transformation. In this paper, we review optical performance monitoring techniques where machine learning algorithms have been applied. Moreover, since many performance monitoring approaches in the optical domain depend on knowledge of the signal type, we also review work for modulation format recognition and bitrate identification. We additionally briefly introduce a neuromorphic approach as an emerging technique that has only recently been applied to this domain.

Experimental study of machine-learning-based detection and location of eavesdropping in end-to-end optical fiber communications

As the significant infrastructure of the communication network, the security of the optical fiber is critical. Optical fiber eavesdropping is a common attack in optical communications, which can easily lead to leakage of information. To deal with this problem, this paper proposes a Machine Learning (ML)-based scheme to detect and locate physical-layer eavesdropping. To improve the efficiency and accuracy of eavesdropping detection, both Optical Performance Monitoring (OPM) data and eye-diagrams are adopted as input data. Three different ML classifiers are designed and tested to realize eavesdropping detection, location and split ratio recognition, respectively. To demonstrate the feasibility of the proposed scheme, an experiment is conducted in end-to-end fiber transmission system with Coherent Optical Orthogonal Frequency Division Multiplexing (CO-OFDM). The eavesdropping is simulated and performed by placing optical coupler in different positions. In addition, different splitting ratios are considered, including 95/5, 90/10. Results indicate that the ML scheme achieves 100% and 92.76% accuracy in eavesdropping detection and location, respectively.

Joint modulation format identification and OSNR estimation method based on trajectory information analysis

We propose a joint modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation method via analyzing the trajectory information of the received adjacent symbols. A uniform grid in the first quadrant of constellation diagram is designed for converting the trajectory information of the received adjacent symbols to the adjacent matrix of corresponding mapped graph structure. Then, we translate the OSNR-independent MFI scheme to an appropriate canonical vector searching optimization problem via improving on the classical cosine similarly algorithm, where the eigenvector associated with the largest eigenvalue of the adjacent matrix is served as discriminated-feature to identify five commonly-used modulation format types, i.e., polarization division multiplexing (PDM)-quadrature phase shift keying (QPSK), PDM=8 quadrature amplitude modulation (QAM), PDM-16QAM, PDM-32QAM, and PDM-64QAM. After exact modulation format type is obtained, we build the relationship between the second-largest eigenvalue of the adjacent matrix and the OSNR value via polynomial regression method, and the least-squares principle (LSP) is utilized to find the best fit line for obtaining the optimal polynomial coefficient. The proposed method is verified by 28 GBaud PDM-QPSK, PDM-8QAM, PDM-16QAM, PDM-32QAM, and PDM-64QAM optical signal numerical simulations, and the results show that could achieve excellent performance on various parameter analysis. Besides, a proof-of-concept experiment is also conducted. More specifically, the proposed method only need one training process and one matrix decomposition, which would provide a superior convenience for future optical performance monitoring.