A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> WOC: <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> BIB001 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> WOC: <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> WOC: <s> A novel joint atmospheric turbulence (AT) detection and adaptive demodulation technique based on convolutional neural network (CNN) are proposed for the OAM-based free-space optical (FSO) communication. The AT detecting accuracy (ATDA) and the adaptive demodulating accuracy (ADA) of the 4-OAM, 8-OAM, 16-OAM FSO communication systems over computer-simulated 1000-m turbulent channels with 4, 6, 10 kinds of classic ATs are investigated, respectively. Compared to previous approaches using the self-organizing mapping (SOM), deep neural network (DNN) and other CNNs, the proposed CNN achieves the highest ATDA and ADA due to the advanced multi-layer representation learning without feature extractors designed carefully by numerous experts. For the AT detection, the ATDA of CNN is near 95.2% for 6 kinds of typical ATs, in cases of both weak and strong ATs. For the adaptive demodulation of optical vortices (OV) carrying OAM modes, the ADA of CNN is about 99.8% for the 8-OAM system over the computer-simulated 1000-m free-space strong turbulent link. In addition, the effects of image resolution, iteration number, activation functions and the structure of the CNN are also studied comprehensively. The proposed technique has the potential to be embedded in charge-coupled device (CCD) cameras deployed at the receiver to improve the reliability and flexibility for the OAM-FSO communication. <s> BIB003 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> WOC: <s> The vortex beam carrying orbital angular momentum (OAM) has attracted great attentions in optical communication field, which can extend the channel capacity of communication system due to the orthogonality between different OAM modes. Generally, atmospheric turbulence can distort the helical phase fronts of OAM beams, which presents a critical challenge to the effective recognition of OAM modes. Recently, convolutional neural network (CNN), as a model of deep learning, has been widely applied to machine vision. In this paper, based on the CNN theory, we make a tradeoff between the computational complexity of the system and the efficiency of recognition by establishing a specially designed six-layer CNN structure in CPU station to efficiently achieve the recognition of OAM mode in turbulent environment through the feature extraction of the received Laguerre–Gaussian beam's intensity distributions. Furthermore, we examine the performances of our designed CNN with respect to various turbulence levels, transmission distances, mode spacings, and we have also compared the performances of recognizing single OAM mode with multiplexed OAM modes. The numerical simulation shows that basing on CNN method, the coaxial multiplexed OAM modes can obtain higher recognizing accuracy about 96.25% even under long transmission distance with strong turbulence. It is anticipated that the results might be helpful for future implementation of high-capacity OAM-based optical communication technology. <s> BIB004 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> WOC: <s> In this paper, we investigate the design and implementation of machine learning (ML)-based demodulation methods in the physical layer of visible light communication (VLC) systems. We build a flexible hardware prototype of an end-to-end VLC system, from which the received signals are collected as the real data. The dataset is available online, which contains eight types of modulated signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), the deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Specifically, the CNN-based demodulator converts the modulated signals to images and recognizes the signals by the image classification. The proposed DBN-based demodulator contains three restricted Boltzmann machines to extract the modulation features. The AdaBoost method includes a strong classifier that is constructed by the weak classifiers with the k -nearest neighbor algorithm. These three demodulators are trained and tested by our online open dataset. The experimental results show that the demodulation accuracy of the three data-driven demodulators drops as the transmission distance increases. A higher modulation order negatively influences the accuracy for a given transmission distance. Among the three ML methods, the AdaBoost modulator achieves the best performance. <s> BIB005
Most of ML for FSO investigations considered CNN. CNN could be used a as a demodulator for a turbo-coded FSO system in the presence of strong atmospheric turbulence BIB001 . The feasibility of the scheme is verified by transmitting a two dimensional digital image. CNN also could be used for joint atmospheric turbulence detection and demodulation for an orbital angular momentum FSO system BIB002 BIB003 . It is proven that this technique has higher accuracy compared with previous approaches. Although vortex beam carrying orbital angular momentum increases capacity, atmospheric turbulence affect it by distorting the helical phase fronts of the propagating beams. Recently, CNN has been designed to combat this effect by feature extraction from the received Laguerre-Gaussian beam's intensity distributions; in the proposed structure, there is a tradeoff between the computational complexity and the efficiency of achieving a good recognition of OAM mode in atmospheric turbulent BIB004 . As the only investigation of CNN in VLC system BIB005 , CNN is used for demodulation. CNN is the classifier of imagery data input, accordingly it converts the modulated signals to images and recognizes them by classifying these images. The only investigated application of regression in VLC is visible light positioning (VLP). A rarely investigated ML structure is the dual-function ML, which could be used applications such as Radio Transformer Network, indoor positioning, etc. This technique combines two ML methods which each of them are proper for a single task. For example, in indoor positioning, different indoor areas could be divided into some parts by a classification algorithm, and then a regression algorithm could be used for exact positioning  .