ORT image and IMT-MIMO-ResNet-based multi-mode multi-parameter joint optical performance monitoring in MDM-EON systems.

Direct Detection (DD) optical performance monitoring (OPM), Modulation Format Identification (MFI), and Baud Rate Identification (BRI) are envisioned as crucial components of future-generation optical networks. They bring to optical nodes and receivers a form of adaptability and intelligent control that are not available in legacy networks. Both are critical to managing the increasing data demands and data diversity in modern and future communication networks (e.g., 5G and 6G), for which optical networks are the backbone. Machine learning (ML) has been playing a growing role in enabling the sought-after adaptability and intelligent control, and thus, many OPM, MFI, and BRI solutions are being developed with ML algorithms at their core. This paper presents a comprehensive survey of the available ML-based solutions for OPM, MFI, and BFI in non-coherent optical networks. The survey is conducted from a machine learning perspective with an eye on the following aspects: (i) what machine learning paradigms have been followed; (ii) what learning algorithms are used to develop DD solutions; and (iii) what types of DD monitoring tasks have been commonly defined and addressed. The paper surveys the most widely used features and ML-based solutions that have been considered in DD optical communication systems. This results in a few observations, insights, and lessons. It highlights some issues regarding the ML development procedure, the dataset construction and training process, and the solution benchmarking dataset. Based on those observations, the paper shares a few insights and lessons that could help guide future research.

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

In this paper, asynchronous complex histogram (ACH)-based multi-task artificial neural networks (MT-ANNs), are proposed to realize modulation format identification (MFI), optical signal-to-noise ratio (OSNR) estimation and fiber nonlinear (NL) noise power estimation simultaneously for coherent optical communication. Optical performance monitoring (OPM) is demonstrated with polarization mode multiplexing (PDM), 16 quadrature amplitude modulation (QAM), PDM-32QAM, as well as PDM-star 16QAM (S-16QAM) for the first time. The range of launched power is −3 to −2 dBm with a fiber link of 160–1600 km. Then, the accuracy of MFI reaches 100%. The average root mean square error (RMSE) of OSNR estimation can reach 0.37 dB. The average RMSE of NL noise power estimation can reach 0.25 dB. The results show that the monitoring scheme is robust to the increase of fiber length, and the solution can monitor more optical network parameters with better performance and fewer training data, simultaneously. The proposed ACH MT-ANN has certain reference significance for the future long-haul coherent OPM system.

In this study, we develop an intelligent constellation diagram monitoring method for fiber optic communications based on a small sample learning network. The method is designed to identify the modulation format, estimate the optical signal-to-noise ratio (OSNR), and predict the fiber transmission distance by analyzing a small amount of constellation diagram data. The model was trained using the C-way-K-shot strategy with the Adam optimization algorithm, with a learning rate of 0.001. In terms of modulation format identification, the model was tested under different OSNR conditions, and the results showed that under low, medium, and high OSNR conditions, the 5-way-3-shot task models achieved average accuracies over 10 iterations of 95.73%, 98.35%, and 97.86%, respectively. In contrast, for the 5-way-5-shot task, the accuracies increased to 98.38%, 99.23%, and 98.74%, respectively. This indicates that, even under low OSNR conditions, the small-sample learning network can achieve a high modulation format recognition rate, demonstrating the model’s fast adaptability and high accuracy.Meanwhile, the OSNR estimation in low-order modulation formats achieves a 100% recognition rate, and for high-order modulation formats, the estimation also performs well. The small-sample learning network demonstrates good performance even with a limited data sample size. The recognition rate of transmission distance estimation for low-order modulation formats, such as BPSK and QPSK, performs well under low, medium, and high OSNR conditions. However, performance in recognizing higher-order modulation formats, such as 64QAM, is poor, highlighting both the adaptability and limitations of the model when dealing with different modulation formats. This study demonstrates the potential of small-sample learning networks for intelligent constellation diagram monitoring in fiber optic communications, especially in terms of their high accuracy and robustness in modulation format identification, OSNR estimation, and fiber link transmission distance prediction. The method effectively utilizes a small sample size to achieve fast and accurate monitoring, thus providing effective technical support for the intelligent management and maintenance of fiber optic communication systems.

In coherent optical communication systems, where multiple modulation formats are mixed and variable, the correct identification of signal modulation formats provides the foundation for subsequent performance improvement using digital algorithms. A modulation format identification (MFI) scheme based on signal constellation diagrams and support vector machine (SVM) is proposed. Firstly, the signal constellation diagrams are divided by the fractal dimension of the weighted linear least squares (WLS-FD) algorithm, and the fractal dimension (FD) in each region is calculated, which is regarded as one of the image features. Then, the feature values of the image in different directions are extracted by the gray-level co-occurrence matrix (GLCM), and their mean and variance are calculated, which is regarded as another feature. Finally, the two features are input into the modulation format classifier constructed by the SVM to achieve MFI in coherent optical communication systems. To verify the feasibility and superiority of the scheme, we compare it with the MFI scheme based on higher-order statistical (HOS) features, GLCM features, and FD features, respectively. Further, we built a 30 GBaud coherent optical communication system with fiber lengths of 80 km and 120 km, where the optical signal-to-noise ratio (OSNR) ranges from 0 dB to 30 dB. The proposed MFI scheme identifies seven modulation formats: QPSK, 8QAM, 16QAM, 32QAM, 64QAM, 128QAM, and 256QAM. The results show that compared with the other three schemes, our proposed scheme has a better identification accuracy at low OSNR. In addition, the identification accuracy of this scheme can reach 100% when the OSNR ≥ 10 dB.

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.

Modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring are essential for elastic optical networks. A method for joint modulation format identification and OSNR monitoring based on ternary neural networks was proposed. Further, a ternary neural network was established, and the constellation images after constant modulus algorithm (CMA) equalization were used as input features. Four commonly used modulation formats were distinguished, including dual-polarization (DP)-QPSK, 8QAM, 16QAM, and 64QAM. A 32G baud simulation system was constructed, and the results were analyzed. First, we investigated the influence of resolution and sample length on the identification accuracy. The values 32 and 7000 were selected for the two parameters, respectively, based on the balance between resource consumption and accuracy. Then, we compared the performance of the binary neural network (BNN), ternary neural network (TNN), and full-precision neural network (FNN). The results indicate that the memory consumption and extraction time of the TNN are similar to those of the BNN, and the accuracy is further improved. Moreover, the robustness of the system was analyzed. The results validate that the proposed method can tolerate fiber nonlinearity (from 500 km to 1500 km). Finally, an experiment was conducted to prove the practicality of the proposed method.

As critical communication infrastructure, optical networks have a vital role in safe and dependable transmission of massive amounts of data, supporting essential societal services. However, these networks are inherently vulnerable to a multitude of deliberate, man-made attacks targeting service disruption at the physical layer. Physical-layer attack techniques can range in their scope and effects, level of sophistication, locality, detectability, etc. An example of a relatively unsophisticated attack method is a deliberate fiber cut, typically targeting critical network elements (e.g., links with the highest betweenness) and resulting in straightforward transmission interruption [1]. More refined attack techniques rely on the insertion of harmful signal (e.g. in- and out-of-band jamming) [2], or on external tampering with the fiber to degrade the transmission quality (e.g., polarization scrambling via fiber squeezing) [3]. Diverse attack techniques cause different effects, which complicates their detectability. For example, some attacks add unfilterable noise, some reduce the power of the affected optical channels, while some inflict changes in the state of polarization too quick for the coherent receiver to compensate [3]. Therefore, monitoring only the spectrum [4], or individual signal parameters such as the power, optical signal-to-noise ratio (OSNR), or presence of errors may result in inaccurate diagnostics and root cause attribution. This obstacle in quick recovery of affected services is further pronounced for newly emerging attack techniques whose effects may deviate from the attack signatures previously known to the network management system [5].The complexity of the evolving physical-layer security landscape and the intricate interplay of different optical performance monitoring (OPM) parameters in the presence of diverse attack methods can greatly benefit from the application of machine learning techniques capable of deep data analysis. In this talk, we present how different data analytics and machine learning approaches can be applied to interpret the OPM data reported from the commercially available coherent receivers to identify anomalous operation and trigger security threat warnings. The analytical techniques are applied to experimental data obtained from an operator’s metropolitan testbed subjected to in- and out-of-band jamming, and external polarization scrambling attacks. We begin with an analysis of the optical signal degradation caused by the different attack methods. We then investigate the application of several supervised learning approaches that, once trained on the experimental data, can detect the presence of an attack and identify its type and intensity. The accuracy of several classifiers is scrutinized, along with the relevance of OPM parameters reported by the coherent receivers and the impact of missing features. To gain insight into the potential of the network to detect emerging, previously unseen attack techniques, we further analyse the performance of unsupervised learning techniques that detect the anomalies in signal parameters introduced by attacks. The presented findings help achieve timely and accurate detection of physical-layer attacks and serve as a prerequisite for fast and effective attack response and network recovery.

A modulation format identification (MFI) method based on the nonlinear power transformation via logistics regression is adopted for coherent optical receives systems. The amplitude variance, fourth power transformation and fast Fourier transform of input signals are utilized for special features extraction in our work. Five typical optical modulation formats (i.e.,16/32/64QAM and Q/8PSK) with the transmission rate of 28 GBaud are numerically simulated to demonstrate the feasibility. The simulation results show that our method has great performance even under low optical signal noise ratio (OSNR). Compared with the MFI algorithm based on Stokes space and asynchronous delay tapped sampling, our MFI algorithm requires less time to achieve similar performance of optical receive systems. Especially, this method exhibits tolerances to the laser linewidth and nonlinearity.

Acoustic measurement of physical properties

In this work, a prior-training-free and low-complexity modulation format identification (MFI) scheme, based on amplitude histogram distributions, was proposed and demonstrated, both numerically and experimentally, for autonomous digital coherent receivers. In the proposed scheme, after having performed power normalization, incoming polarization division multiplexed (PDM) signals were classified into QPSK, 8QAM, 16QAM, 32QAM and 64QAM signals, according to their ratios. Ratios were defined according to specific features of their amplitude histograms. The proposed MFI scheme used only amplitude information. As such, it was insensitive to carrier phase noise. Furthermore, the proposed scheme did not require any prior information, such as optical signal-to-noise ratio (OSNR). The performance of the proposed MFI scheme was numerically verified using 28GBaud PDM-QPSK/-8QAM/-16QAM/-32QAM/-64QAM signals. The numerical simulation results showed that the proposed scheme was able achieve a 100% correct identification rate for all five modulation formats when their OSNR values were higher than the thresholds corresponding to the 20% FEC correcting bit error rate (BER) of 2.4 × 10−2. To further explore the effectiveness of the proposed MFI scheme, proof-of-concept experiments in 28GBaud PDM-QPSK/-8QAM/-16QAM, and 21.5GBaud PDM-32QAM transmission systems were also undertaken, which showed that the proposed scheme as robust against fiber nonlinearities. To explore the scheme’s feasibility for use in practical transmission systems, the computational complexity analysis of the proposed scheme was conducted. It showed that, compared with relevant MFI schemes, the proposed MFI scheme was able to significantly reduce the computational complexity.

Cost-effective OSNR monitoring with large chromatic dispersion tolerance using random forest for intermediate nodes

In this work, we investigate the effects of estimation uncertainties in the optimization of the optical signal-to-noise ratio (OSNR) at the wavelength division multiplexing (WDM) optical networks. The OSNR optimization increases the network throughput and energy efficiency and it enables to minimize the number of retransmissions by higher layers. The OSNR optimization algorithms are related to the optical channel estimation performed by the combinations of optical performance monitors (OPM) measurements, analytical models and numerical simulations. Furthermore, the estimation uncertainties are introduced by several factors such as the availability of monitoring information, monitoring accuracy, imperfection of physical layer and dynamic of channel allocation. Our numerical results have demonstrated the relation between the uncertainties in the channel error estimation, normalized mean squared error (NMSE) and power penalty. The developed methodology can be utilized to determinate the maxim level of acceptable uncertainties in the channel error estimation according the power penalty permitted in the OSNR optimization.

The desirable features of future optical high-capacity networks include higher stability, reconfigurability and security. In order to enable robust and self-managed automated operation, the network itself should be able to monitor the physical state of the network, automatically diagnose and repair the network, dynamically allocate resources, and redirect traffic. Optical performance monitoring can agilely provide valuable information such that routing tables themselves can dynamically reflect the state of the physical links to the network controller. The key features of the desired monitor include simplicity in implementation, cost-effectiveness, transparency to data formats and rates, and isolation of the specific impairment from the others. We propose and demonstrate novel optical performance monitoring techniques using cost-effective photonic components and artificial neural network-assisted signal processing to continuously monitor multiple impairments and to accommodate different high-speed advanced modulation formats. These monitoring techniques can facilitate rapid identification and isolation of impairments to enhance the dynamic management of the optical high-capacity networks.

Optical performance monitoring (OPM) aims to estimate the amount of distortion in optical networks. Its importance relies on building robust and efficient networks with self and dynamic diagnosis. In this work, channel impairment monitoring is investigated for optical wireless communication. The monitoring is achieved using a support vector machine (SVM) regressor. A cost-effective and straightforward acquisition system is used to build the training features, which are asynchronous amplitude histograms. Three different parameters related to three common channel impairments are monitored using these features:optical signal-to-noise ratio (OSNR), visibility range, and $\xi$ parameter related to pointing error. First, each parameter is monitored when there is only one isolated channel impairment. Then, each parameter is monitored when two and three jointly channel impairments occur. The results showed that using this low complex machine learning (ML) technique, the achieved prediction accuracy was very high ( $>$ 0.98) for most channel conditions except for the monitoring of the OSNR parameter, where the prediction accuracy dropped to 0.86 under harsh channel conditions. Moreover, the superiority of ML-based techniques is compared with non-ML-based techniques for the OSNR parameter monitoring. The results indicated that the ML-based technique achieved high prediction accuracy than the non-ML-based technique, especially for harsh channel conditions.