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.

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.

An $m$ -ary adaptive demodulator based on machine learning for light beams carrying orbital angular momentums (OAMs) over free-space turbulence channels is proposed and demonstrated. Benefiting from natural advantages in the image recognition, convolutional neural network (CNN) is selected to construct the adaptive demodulator. Without extra space light modulators and digital signal processing at the reception, the adaptive demodulator transforms the sequence of intensity patterns of received Laguerre-Gaussian beams carrying different OAM modes into initial signals efficiently. As comparison, K-nearest neighbor (KNN), naive Bayes classifier (NBC), and back-propagation artificial neural network (BP-ANN) are also studied. Furthermore, the demodulating accuracy of 4-, 8-, and 16-ary OAM is investigated with the comprehensive consideration of the atmospheric turbulence, OAM mode spacing, and transmission distance. The simulation results show that the demodulating error rate (DER) of CNN outperforms KNN, NBC, and BP-ANN, especially under stronger turbulence and longer distance. The DER of CNN is ~0.86% for the 1000-m 8-OAM system under strong turbulence, ~30 % less than those of KNN, NBC, and BP-ANN.

With the continuous addition of an abundant of heterogeneous devices, the limitation of task delay has become an obstacle to the development of the Industrial Internet of Things (IIoT). Task offloading based on edge computing can provide low-latency computing services for these tasks. However, in the actual IIoT scenario, in contrast to cloud computing, edge computing has limited resources and computing capabilities. Resource-constrained edge resources cannot meet the offloading requirements of massive industrial devices. In this article, we propose an optimal joint offloading scheme based on resource occupancy prediction for the problem of computing offloading with limited edge resources. The scheme is divided into two parts, including edge resource occupancy prediction and task offloading. Simultaneously, considering multitask and the limitations of edge resources, gate recurrent unit (GRU) is used to predict the occupancy of edge resources. Formulating an optimal strategy of task offloading by using a reinforcement learning algorithm according to the network state and predicted results. The simulation results show that the scheme can effectively reduce the average delay of tasks, while minimizing the task offloading failure rate.