A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> D. Convolutional Neuronal Network <s> 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. <s> BIB001 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> D. Convolutional Neuronal Network <s> In this paper, a novel turbo-coded 16-ary orbital angular momentum - shift keying-free space optical (OAM-SK-FSO) communication system combining a convolutional neural network (CNN) based adaptive demodulator under strong atmospheric turbulence is proposed for the first time. The feasibility of the scheme is verified by transmitting a 256-grayscale two-dimensional digital image. The bit error ratio (BER) performance of the system is investigated and the effect of different factors such as turbulence strength, propagation distance, code rate, length of random interleaver and length of bit interleaver is also taken into account. An advanced encoder/decoder structure and mapping scheme are applied to diminish the influence of CNN misclassification and reduce the BER effectively. With the optimal encoder/decoder structure and CNN model settings, the BER varies from 0 to 4.89×10-4 when the propagation distance increases from 200m to 1000m for a given turbulence strength Cn2 equals 5×10-14m-2/3. For a determined propagation distance equals 400m, the BER ranges from 0 to 4.01×10-4 when Cn2increases from 1×10-15m-2/3 to 4×10-13m-2/3. Our numerical simulations demonstrate that the proposed system can provide better BER performance under strong atmospheric turbulence and conditions when the classification ability of CNN is limited. <s> BIB002 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> D. Convolutional Neuronal Network <s> In a cognitive, heterogeneous, optical network, it would be important to identify physical layer information, especially the modulation formats of transmitted signals. The modulation format information is also indispensable for carrier-phase-recovery in a coherent optical receiver. Because constellation diagrams of modulation signals are susceptible to various noises, we utilize a convolutional neural network to process the amplitude data after the modulation-format-agnostic clock recovery. Furthermore, for the carrier-phase-recovered data, we use the clustering method based on a fast search and find the density peaks to classify the constellation clusters and use the k-nearest-neighbor method to label the samples. The proposed receiver system has a simple architecture to identify the modulation format based on the amplitude information and can track fast changes of the signals to improve the accuracy of the symbol decision. We have demonstrated this experimentally and have achieved remarkable BER improvement. <s> BIB003 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> D. Convolutional Neuronal Network <s> Fiber Specklegram Sensors (FSSs) are highly sensitive to external perturbations, however, trying to locate perturbation's position remains as a barely addressed study. In this work, a system able to classify perturbations according to the place they have been caused along a multimode optical fiber has been designed. As proof of concept, a multimode optical fiber has been perturbated in different points, recording the videos of the perturbations in the speckle pattern, processing these videos, training with them a machine learning algorithm, and classifying further perturbations based on the spatial locations they were generated. The results show classifications up to 99% when the system has to categorize among three different locations lowering to 71% when the locations rise to ten. <s> BIB004 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> D. Convolutional Neuronal Network <s> To address the open and diverse situation of future optical networks, it is necessary to find a methodology to accurately estimate the value of a target quantity in an optical performance monitor (OPM) depending on the high-level monitoring objectives declared by the network operator. Using machine learning techniques partially enables a trainable OPM; however, it still requires the feature selection before the learning process. Here, we show the OPM that uses a convolutional neural network (CNN) with a digital coherent receiver to deal with the abundance of training data required for convergence and pre-processing of input data by human engineers needed for feature (representation) extraction. To proof a concept of the OPM based on CNN, we experimentally demonstrate that a CNN can learn an accurate optical signal-to-noise-ratio (OSNR) estimation functionality from asynchronously sampled data right after intradyne coherent detection. We evaluate bias errors and standard deviations of a CNN-based OSNR estimator for six combinations of modulation formats and symbol rates and confirm that the proposed OSNR estimator can provide accurate estimation results (<0.4 dB bias errors and standard deviations). Additionally, we investigate filters in the trained CNN to reveal what the CNN learned in the training phase. This is a valuable step toward designing autonomous "self-driving" optical networks. <s> BIB005 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> D. Convolutional Neuronal Network <s> We apply deep graph convolutional neural networks for Quality-of-Transmission estimation of unseen network states capturing, apart from other important impairments, the inter-core crosstalk that is prominent in optical networks operating with multicore fibers. <s> BIB006
Definition: MLP is a fully connected NN, which is prone to over fitting; for solving this problem, addition of a regularization term related to NN weights is suggested. CNN is a class of DNN (a regularized version of MLP), which is mostly used in analyzing image signals, which presents a different regularization approach. CNN considers hierarchical pattern in data, and assembles more complex patterns using smaller and simpler patterns. The basic idea of connectivity pattern between neurons in CNN is taken from resembles the organization of the animal visual cortex. Different cortical neurons have limited different stimulation areas (receptive fields) that are partially overlapped and cover the entire visual field. CNN is mostly used for image processing. Due to its high computation, few works considered this technique, and other NNs are preferred to it. Fiber OC: Despite previous ML algorithms, the main application of CNN is optical performance monitoring/MFI. By using multi-layer self-learning, CNN can discover intrinsic features of the original image, identify the image, and achieve great success in the OC applications such as optical performance monitoring and MFI BIB002 . CNN can process constellation diagram as raw data (i.e., pixel points of an image) from the perspective of image processing, without manual intervention nor data statistics. Therefore, eye diagrams or AHHs could be used as inputs of CNN for OSNR monitoring and MFI BIB001 ; this approach outperformed decision tree, KNN, SVM, and ANN. The MFI is indispensable for carrier phase recovery in a coherent optical receiver. Because constellation diagrams of modulation signals are susceptible to various noises; in such cases, a good idea is use of hybrid ML technique, in the sense that first a ML MFI based clock recovery could be developed, and then a CNN could be utilized to process the amplitude data BIB003 . Network: Considering CNN applications in OC networks, they include identification/estimation of target quantities, OPM BIB005 , and QoT estimation BIB006 . Despite other ML algorithms (used for OPM, and QoT estimation), CNN deals with the abundance of training data required for convergence and pre-processing of input data by human engineers needed for feature extraction. CNN could also be used for detecting Fiber link failures (to which the Fiber speckle gram sensors are highly sensitive) BIB004 . At first different images are taken from perturbed fibers, then the CNN is learned, then a new image is checked for perturbations. This process also could be done by ANN, but a bit change in input features is required, because they are fundamentally different.