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.

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.

A data-driven fiber channel modeling method based on deep learning (DL) is introduced in an optical communication system. In this study, bidirectional long short-term memory (BiLSTM) is selected from a diverse range of DL algorithms to perform fiber channel modeling for on—off keying and pulse amplitude modulation 4 signals. Compared with the conventional model-driven split-step Fourier (SSF)-based method, the proposed method yields similar results based on the comprehensive comparison of multiple characteristics associated with the generated optical signals, including the optical amplitude and phase waveforms in the time domain, optical spectrum components in the frequency domain, and eye diagrams after detection in the electrical domain. Additionally, the effects of multiple factors on the modeled fiber channel have also be investigated, including fiber length, fiber nonlinearity, dispersion, data pattern, pulse shaping, and sample rate. The satisfactory fitting results and acceptable mean square errors indicate that the approximate transfer function of the fiber channel is learned by the BiLSTM. Moreover, compared with repetitive iteration SSF, the computing time is significantly reduced by the BiLSTM owing to its independence on fiber length and insensitivity to data size and launch power. Our aim is to demonstrate the BiLSTM is comparable with the conventional model-driven SSF-based method for direct-detection optical fiber system. We think the proposed method could be a supplementary technique that can be used for the existing simulation system and could also be a potential option for future simulation methods.

Digital twin (DT) has revolutionized optical communication networks by enabling their full life-cycle management, including planning, prediction, optimization, upgrade, and troubleshooting. The rapid development of DT applications for optical networks, from concepts and architecture to simulation and experimental validation, has been exceptional. As a real-time virtual replica of physical optical networks, the DT of optical networks can be divided into three parts: real-time monitoring, mirror modeling, and automatic control. Recent advancements in each part have individually propelled the network towards intelligence and automation. In this paper, we make a comprehensive survey on this timely topic and illustrate the principal evolutions within these three parts. This will be followed by an overview of ongoing DT research in optical communications and networking, as well as an outlook on potential future directions for further investigation.

Network slicing has been proposed as an innovative concept to partition a physical network into different configurable slices, thereby allowing the network to meet the diverse requirements of expected services in the 5G era. Existing network-slicing schemes focus almost exclusively on the partitioning of the core network or the radio access network. However, to achieve greater flexibility and better resource utilization in service provisioning, the potential of network slicing in the 5G optical mobile fronthaul (MFH) network should be jointly exploited. To address this challenge, we propose a flexible hierarchical edge cloud architecture to enable 5G optical MFH network slicing. Additionally, a corresponding integrated network resource management scheme is proposed to support various quality-of-service (QoS) requirements and diverse resource requests of the network slices. Simulation results indicate that the proposed scheme is able to jointly allocate the bandwidth resources to network slices and realize cloud-computing offloading to meet various QoS requirements, and therefore reduce the fronthaul bandwidth burden.