A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> The popularity of multimedia applications has resulted in increased interest in efficient implementation of multicasting. Wavelength division multiplexing (WDM) is a promising technology for backbone networks, because they provide high bandwidth and methods for control and management. Since not every switches are equipped with wavelength conversion facility, it is a really important issue to design multicast trees, which result in less blocking-probability in the networks. In this paper we propose a reinforcement learning algorithm for the multicast routing and wavelength assignment (MRWA) problem. This distributed algorithm is compared with a multicast algorithm used by a well-known multicast protocol (MOSPF). The proposed algorithm is a good solution to the (MRWA) problem, since it reduces the blocking probability of the multicast communication requests. <s> BIB001 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Optical burst switching (OBS) is a promising technology that exploits the benefits of optical communication and supports statistical multiplexing of data traffic at a fine granularity making it a suitable technology for the next generation Internet. Development of efficient algorithms for path selection and wavelength selection is crucial in minimizing the burst loss probability (BLP) in OBS networks. In this paper, we present novel Reinforcement Learning algorithms for path selection and wavelength selection in the context of OBS networks. We develop an online path selection algorithm based on Q-learning to minimize the BLP by choosing an optimal path among a set of predetermined routes between every pair of ingress and egress nodes. We also propose a Q-learning algorithm for wavelength selection that selects an optimal wavelength among the available wavelengths in a pre-routed path with an objective of minimizing the BLP. We assume no wavelength conversion and buffering to be available at the core nodes of the OBS network. We simulate the proposed algorithms under dynamic load to demonstrate that they reduce the BLP compared to the best known adaptive techniques for path selection and wavelength selection available in the literature. <s> BIB002 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Optical burst switching (OBS) is an optical switching paradigm which offers a good tradeoff between the traditional optical circuit switching (OCS) and optical packet switching (OPS) since it has the relatively easy implementation of the first and the efficient bandwidth utilization of the second. Hence, OBS is a promising technology for the next generation optical Internet. A buffer-less OBS network can be implemented using ordinary optical communication equipment without the need for either wavelength converters or optical memories. However, OBS networks suffer from a relatively high blocking probability, a primary metric of interest, because of contention. In this paper we propose a new contention resolution scheme for buffer-less OBS networks using deflection routing and reinforcement learning agents to dynamically assign an appropriate offset time (OT) to each burst in order to reduce losses caused, for example, by insufficient offset time (IOT) in case only deflection is used. Simulation results demonstrate that our approach reduces effectively blocking probability, whereas it maintains a reasonable end-to-end delay for each burst. Hence, it establishes an appropriate tradeoff between loss rate and delay. <s> BIB003 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We present and evaluate a reinforcement learning-based RWA algorithm for all-optical networks subject to physical impairments. The technique is suitable for decentralized networks and is compared with other techniques with similar computational complexity. <s> BIB004 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We consider event dependent routing algorithms for on-line explicit source routing in MPLS networks. The proposed methods are based on load shared sequential routing in which load sharing factors are updated using learning algorithms. The learning algorithms we employ are either based on learning automata or on online learning algorithms that were originally devised for solving the adversarial multi-armed bandit problem. While simple to implement, the performance of the proposed learning algorithms in terms of blocking probability compares favorably with the performance of other event dependent routing methods proposed for MPLS routing such as the Success-to-the-top algorithm. We demonstrate the convergence of one of the learning algorithms to the user equilibrium within a set of discrete event simulations. <s> BIB005 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Optical burst switching (OBS) is a promising technology that exploits the benefits of optical communication and supports statistical multiplexing of data traffic at a fine granularity making it a suitable technology for the next generation Internet. Contention among the bursts that arrive simultaneously at a core node leads to burst loss which affects the throughput of higher layer traffic. Development of efficient algorithms for path selection and wavelength selection is crucial to minimize the burst loss probability (BLP) in OBS networks. In this paper, we formulate path selection and wavelength selection in OBS networks as a multi-armed bandit problem and discuss the difficulties to solve them optimally. We propose algorithms based on Q-learning to solve these problems near-optimally. At an egress node, the path selection algorithm evaluates the Q values for a set of precomputed paths and chooses a path that corresponds to minimum BLP. Similarly, Q-learning algorithm for wavelength selection selects a wavelength in a pre-routed path such that the BLP is minimized. We do not assume wavelength conversion and buffering at the core nodes and hence, selection of path and wavelength is done only at the edge nodes. We simulate the proposed algorithms under dynamic load to demonstrate that they reduce the BLP compared to the other adaptive algorithms available in the literature. <s> BIB006 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Optical burst switching (OBS) is a promising switching paradigm for the next generation Internet. A buffer-less OBS network can be implemented simply and cost-effectively without the need for either wavelength converters or optical buffers which are, currently, neither cost-effective nor technologically mature. However, this type of OBS networks suffers from relatively high loss probability caused by wavelength contentions at core nodes. This issue could prevent or, at least, delay the adoption of OBS networks as a solution for the next generation optical Internet. Deflection routing is one of the contention resolution approaches that have been proposed to tackle this problem. In addition to be cost-effective, it is also efficient in reducing loss probability, especially with low and moderate traffic loads. In this paper, we propose an adaptive reinforcement learning-based deflection routing scheme (RLDRS) which focuses on the route selection issue by choosing the optimal alternative output port in terms of both loss probability and delay when deflection is performed. Moreover, RLDRS limits the number of authorized deflections of each burst in order to reduce the additional traffic caused by deflection routing and to prohibit excessive deflections. Simulation results show that RLDRS reduces effectively loss probability and outperforms shortest path deflection routing (SPDR). <s> BIB007 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Optical Burst Switching (OBS) is a promising switching paradigm for the next generation Internet. A bufferless OBS network can be implemented simply and cost-effectively without the need for either wavelength converters or optical buffers which are, currently, neither cost-effective nor technologically mature. However, this type of OBS networks suffers from relatively high loss probability caused by wavelength contentions at core nodes. This issue could prevent or, at least, delay the adoption of OBS networks as a solution for the next generation optical Internet. To enhance the performance of buffer-less OBS networks, we propose two approaches: a) a proactive multi-path approach, called Reinforcement Learning-based Alternative Routing (RLAR), that tries to prevent wavelength contentions; and b) an approach, called Integrated Reinforcement Learning-based Routing and Contention Resolution (IRLRCR) that deals with wavelength contentions proactively and reactively conjointly. We also show relative QoS differentiation capability of the proposed schemes. Simulation results show the ability of the proposed approaches to reduce loss probability and to provide Quality of Service (QoS) differentiation. <s> BIB008 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> An important issue of research in optical burst switching (OBS) networks is to minimize the loss of bursts due to contention at the intermediate nodes. These contention losses can be minimized with the design of efficient path selection algorithms at the ingress node. Path selection algorithms that learn the optimal path dynamically with the changing traffic conditions outperform the deterministic path selection algorithms. Usually in the single agent path selection algorithms, a path is selected by the agent based on the feedback received at the ingress node which does not capture the effect of the paths selected by the other nodes in the network. We develop a multi-agent approach for path selection that includes the effect of the selection made by all the other nodes in the network. The proposed path selection algorithm uses agents at different source nodes to collectively learn the network dynamics and select the best outgoing path for each burst. We present simulation results to demonstrate the effectiveness of the proposed algorithm over the other similar algorithms in the literature. <s> BIB009 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> A Fractionally-Spaced Clustering Based Equalizer is proposed for the electronic equalization of optical channels. Equalization is treated as a classification task. The proposed approach outperforms the recursive Volterra DFE, as it is demonstrated by simulation. <s> BIB010 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> A k-means algorithm for phase recovery of three, 50 Mbaud, 8PSK subcarrier multiplexed signals at 5 GHz for optical phase-modulated radio-over-fiber is proposed and experimentally demonstrated after 40 km of single mode fiber transmission. <s> BIB011 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Highest reported bit rate of 2.5 Gb/s for optically phase-modulated radio-over-fiber (RoF) link, employing digital coherent detection, is demonstrated. Demodulation of 3×2.5 Gb/s quadrature phase-shift-keying modulated wavelength-division-multiplexed RoF channels is achieved after 79 km of transmission through deployed fiber. Error-free performance (bit-error rate corresponding to 10-4) is achieved using a digital coherent receiver in combination with a K-means algorithm for radio-frequency phase recovery. <s> BIB012 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We consider a decentralized cognitive radio network in which autonomous secondary users seek spectrum opportunities in licensed spectrum bands. We assume that the primary users' channel occupancy follows a Markovian evolution, and formulate the spectrum sensing problem as a Decentralized Partially Observable Markov Decision Process (DEC-POMDP). We develop a distributed Reinforcement Learning (RL) algorithm that allows each autonomous cognitive radio to distributively learn its own spectrum sensing policy. The resulting decentralized sensing policy enables secondary users to non-cooperatively reach an equilibrium that leads to high utilization of idle channels while minimizing the collisions among secondary cognitive radios. Moreover, we propose a decentralized channel access policy that permits controlling, with high accuracy, the collision probability with primary users. Our numerical results validate the robustness of this collision probability control as the sensing noise changes. They also show the efficiency of the proposed learning algorithm in improving the spectrum utilization. <s> BIB013 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We present a self-adaptive framework to process lightpath requests on a packet switching optical network. Our framework takes care of issues regarding lightpath setting up, resource reservation and load balancing. The framework is composed of a dimensioning phase that adds up new resources to an initial topology and a learning phase that provides routing intelligence to efficiently use the dimensioned resources. The proposed framework is tested on a realistic topology achieving a probability of packet loss of 1×10-4. <s> BIB014 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> In this paper, we propose a Q-learning based deflection routing algorithm that may be employed to resolve contention in optical burst-switched networks. The main goal of deflection routing is to successfully deflect a burst based only on a limited knowledge that network nodes possess about their environment. Q-learning, one of the reinforcement learning algorithms, has been proposed in the past to help generate deflection decisions. The complexity of existing reinforcement learning-based deflection routing algorithms depends on the number of nodes in the network. The proposed algorithm scales well for larger networks because its complexity depends on the node degree rather than the network size. The algorithm is implemented using the ns-3 network simulator. Simulation results show that it has comparable performance to an existing reinforcement learning deflection routing scheme while having lower memory requirements. <s> BIB015 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We propose a novel technique for simultaneous multi-impairment monitoring and autonomous bit-rate and modulation format identification (BR-MFI) in next-generation heterogeneous fiber-optic communication networks by using principal component analysis-based pattern recognition on asynchronous delay-tap plots. The results of numerical simulations performed for three commonly used modulation formats at two different bit-rates each demonstrate simultaneous and independent monitoring of optical signal-to-noise ratio, chromatic dispersion, and differential group delay with mean errors of 1 dB, 4 ps/nm, and 1.6 ps, respectively, without knowing the signal's bit-rate and modulation format. Similarly, the results for joint BR-MFI validate accurate identification of all the bit-rates and modulation formats despite the presence of various network impairments. The effects of fiber nonli-nearity and transmitter variations on the performance of the proposed technique are also investigated. <s> BIB016 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> While transparent optical networks become more and more popular as the basis of the Next Generation Internet (NGI) infrastructure, such networks raise many security issues because they lack the massive use of optoelectronic monitoring. To increase these networks' security, they will need to be equipped with proactive and reactive mechanisms to protect themselves not only from failures and attacks but also from ordinary reliability problems. This work presents a novel self-healing framework to deal with attacks on Transparent Optical Packet Switching (TOPS) mesh networks. Contrary to traditional approaches which deal with attacks at the fiber level, our framework allows to overcome attacks at the wavelength level as well as to understand how they impact the network's performance. The framework has two phases: the dimensioning phase (DP) dynamically determines the optical resources for a given mesh network topology whereas the learning phase (LP) generates an intelligent policy to gracefully overcome attacks in the network. DP uses heuristic reasoning to engineer the network while LP relies on a reinforcement learning algorithm that yields a self-healing policy within the network. We use a Monte Carlo simulation to analyze the performance of the aforementioned framework not only under different types of attacks but also using three realistically sized mesh topologies with up to 40 nodes. We compare our framework against shortest path (SP) and multiple path routing (MPR) showing that the self-organized routing outperforms both, leading to a reduction in packet loss of up to 88% with average packet loss rates of 1x10^-^3. Finally, some conclusions are presented as well as future research lines. <s> BIB017 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> In this Letter, we develop the Stokes space-based method for modulation format identification by combing power spectral density and a cluster analysis to identify quadrature amplitude modulation(QAM) and phase-shift keying(PSK) signals. Fuzzy c-means and hierarchical clustering algorithms are used for the cluster analysis.Simulations are conducted for binary PSK, quadrature PSK, 8PSK, 16-QAM, and 32-QAM signals. The results demonstrate that the proposed technique can effectively classify all these modulation formats, and that the method is superior in lowering the threshold of the optical signal-to-noise ratio. Meanwhile, the proposed method is insensitive to phase offset and laser phase noise. <s> BIB018 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We propose a K-means clustering-based approach to plan for the deployment of greenfield passive optical networks (PON) aiming to minimize the total deployment cost. Studies show that the proposed approach is effective to significantly reduce the deployment cost compared to a benchmark random-cut approach. <s> BIB019 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> In this paper, tools from machine learning community, such as Bayesian filtering and expectation maximization parameter estimation, are presented and employed for laser amplitude and phase noise characterization. We show that phase noise estimation based on Bayesian filtering outperforms conventional time-domain approach in the presence of moderate measurement noise. Additionally, carrier synchronization based on Bayesian filtering, in combination with expectation maximization, is demonstrated for the first time experimentally. <s> BIB020 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We propose a novel technique for modulation format identification (MFI) in digital coherent receivers by applying deep neural network (DNN) based pattern recognition on signals’ amplitude histograms obtained after constant modulus algorithm (CMA) equalization. Experimental results for three commonly-used modulation formats demonstrate MFI with an accuracy of 100% over a wide optical signal-to-noise ratio (OSNR) range. The effects of fiber nonlinearity on the performance of MFI technique are also investigated. The proposed technique is non-data-aided (NDA) and avoids any additional hardware on top of standard digital coherent receiver. Therefore, it is ideal for simple and cost-effective MFI in future heterogeneous optical networks. <s> BIB021 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> A powerful machine learning detector based on the k-nearest neighbors (KNN) algorithm is proposed to overcome system impairments. The zero-dispersion link (ZDL), dispersion managed link (DML), and dispersion unmanaged link (DUL) are considered. Meanwhile, an improved algorithm, the distance-weight KNN, is introduced, which outperforms the conventional maximum likelihood-post compensation approach. The numerical results show that KNN is feasible for overcoming various impairments, especially for non-Gaussian symmetric noise, such as laser phase noise and nonlinear phase noise in the ZDL or DML. <s> BIB022 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We introduce low complexity machine learning method method (based on lasso regression, which promotes sparsity, to identify the interaction between symbols in different time slots and to select the minimum number relevant perturbation terms that are employed) for nonlinearity mitigation. The immense intricacy of the problem calls for the development of "smart" methodology, simplifying the analysis without losing the key features that are important for recovery of transmitted data. The proposed sparse identification method for optical systems (SINO) allows to determine the minimal (optimal) number of degrees of freedom required for adaptive mitigation of detrimental nonlinear effects. We demonstrate successful application of the SINO method both for standard fiber communication links (over 3 dB gain) and for few-mode spatial-division-multiplexing systems. <s> BIB023 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Techniques from the machine learning community are reviewed and employed for laser characterization, signal detection in the presence of nonlinear phase noise, and nonlinearity mitigation. Bayesian filtering and expectation maximization are employed within nonlinear state-space framework for parameter tracking. <s> BIB024 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> In this brief, kernel principal component analysis (KPCA) is reinterpreted as the solution to a convex optimization problem. Actually, there is a constrained convex problem for each principal component, so that the constraints guarantee that the principal component is indeed a solution, and not a mere saddle point. Although these insights do not imply any algorithmic improvement, they can be used to further understand the method, formulate possible extensions, and properly address them. As an example, a new convex optimization problem for semisupervised classification is proposed, which seems particularly well suited whenever the number of known labels is small. Our formulation resembles a least squares support vector machine problem with a regularization parameter multiplied by a negative sign, combined with a variational principle for KPCA. Our primal optimization principle for semisupervised learning is solved in terms of the Lagrange multipliers. Numerical experiments in several classification tasks illustrate the performance of the proposed model in problems with only a few labeled data. <s> BIB025 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in digital coherent receivers. The proposed technique automatically extracts OSNR and modulation format dependent features of AHs, obtained after constant modulus algorithm (CMA) equalization, and exploits them for the joint estimation of these parameters. Experimental results for 112 Gbps polarization-multiplexed (PM) quadrature phase-shift keying (QPSK), 112 Gbps PM 16 quadrature amplitude modulation (16-QAM), and 240 Gbps PM 64-QAM signals demonstrate OSNR monitoring with mean estimation errors of 1.2 dB, 0.4 dB, and 1 dB, respectively. Similarly, the results for MFI show 100% identification accuracy for all three modulation formats. The proposed technique applies deep machine learning algorithms inside standard digital coherent receiver and does not require any additional hardware. Therefore, it is attractive for cost-effective multi-parameter estimation in next-generation elastic optical networks (EONs). <s> BIB026 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <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> BIB027 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We investigate a machine-learning technique that predicts whether the bit-error-rate of unestablished lightpaths meets the required threshold based on traffic volume, desired route and modulation format. The system is trained and tested on synthetic data. <s> BIB028 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We have developed a low complexity machine learning based nonlinear impairment equalization scheme and demonstrated its successful performance in SDM transmission links achieving compensation of both inter- and intra- channel Kerr-based nonlinear effects. The method operates in one sample per symbol and in one computational step. It is adaptive, i.e. it does not require a knowledge of system parameters, and it is scalable to different power levels and modulation formats. The method can be straightforwardly expanded to multi-channel systems and to any other type of nonlinear impairment. <s> BIB029 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We examine EDFA power excursions during three defragmentation methods of flexgrid super-channels. Using a machine learning approach, we demonstrate automatic and dynamic adjustments of pre-EDFA power levels, and show the mitigation of post-EDFA power discrepancy among channels by over 62%. <s> BIB030 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> A Stokes-space modulation format classification (MFC) technique is proposed for coherent optical receivers by using a non-iterative clustering algorithm. In the clustering algorithm, two simple parameters are calculated to help find the density peaks of the data points in Stokes space and no iteration is required. Correct MFC can be realized in numerical simulations among PM-QPSK, PM-8QAM, PM-16QAM, PM-32QAM and PM-64QAM signals within practical optical signal-to-noise ratio (OSNR) ranges. The performance of the proposed MFC algorithm is also compared with those of other schemes based on clustering algorithms. The simulation results show that good classification performance can be achieved using the proposed MFC scheme with moderate time complexity. Proof-of-concept experiments are finally implemented to demonstrate MFC among PM-QPSK/16QAM/64QAM signals, which confirm the feasibility of our proposed MFC scheme. <s> BIB031 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Abstract We propose a machine learning-based nonsymmetrical demodulation technique relying on clustering to mitigate time-varying distortions derived from several impairments such as IQ imbalance, bias drift, phase noise and interchannel interference. Experimental results show that those impairments cause centroid movements in the received constellations seen in time-windows of 10k symbols in controlled scenarios. In our demodulation technique, the k-means algorithm iteratively identifies the cluster centroids in the constellation of the received symbols in short time windows by means of the optimization of decision thresholds for a minimum BER. We experimentally verified the effectiveness of this computationally efficient technique in multicarrier 16QAM Nyquist-WDM systems over 270 km links. Our nonsymmetrical demodulation technique outperforms the conventional QAM demodulation technique, reducing the OSNR requirement up to ∼0.8 dB at a BER of 1 × 10−2 for signals affected by interchannel interference. <s> BIB032 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> In this work, we proposed two k-means-clustering-based algorithms to mitigate the fiber nonlinearity for 64-quadrature amplitude modulation (64-QAM) signal, the training-sequence assisted k-means algorithm and the blind k-means algorithm. We experimentally demonstrated the proposed k-means-clustering-based fiber nonlinearity mitigation techniques in 75-Gb/s 64-QAM coherent optical communication system. The proposed algorithms have reduced clustering complexity and low data redundancy and they are able to quickly find appropriate initial centroids and select correctly the centroids of the clusters to obtain the global optimal solutions for large k value. We measured the bit-error-ratio (BER) performance of 64-QAM signal with different launched powers into the 50-km single mode fiber and the proposed techniques can greatly mitigate the signal impairments caused by the amplified spontaneous emission noise and the fiber Kerr nonlinearity and improve the BER performance. <s> BIB033 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Optical spectrum (OS) is a vital characteristic of optical signals. Ultra-high resolution (UHR) OS can provide more detailed and accurate information for optical performance monitoring and optical link quality diagnosis. By comparing actual signal UHR-OS observed at in-line monitoring points with the theoretical ideal ones, various signal distortions can be readily identified and more accurately estimated. But in the future flexible heterogeneous optical networks optical signals with different symbol rates, modulation formats and pulse shaping schemes may coexist in the same system. Hence the ideal reference OS of the channel to be monitored can’t be assumed to be fixed or known in advance. It may also be impossible to undertake a reference OS measurement at or near the transmitter as the route path may be dynamically generated. To solve this problem we proposed an automatic ideal reference optical spectrum retrieval (OSR) method according to the actually observed ones. The OSR method can tolerate large OS distortions due to non-ideal optical links or transmitters by the integration of two machine learning techniques, namely unsupervised principle component analysis (PCA) and supervised multiclass support vector machines (SVMs) for feature extraction and UHR-OS classification, respectively. Extensive simulations conducted for nine types of optical signals commonly used show that this method performs very well in the presence of various significant distortions caused by non-ideal optical links or transmitters. <s> BIB034 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> The nonlinear Shannon capacity limit has been identified as the fundamental barrier to the maximum rate of transmitted information in optical communications. In long-haul high-bandwidth optical networks, this limit is mainly attributed to deterministic Kerr-induced fiber nonlinearities and from the interaction of amplified spontaneous emission noise from cascaded optical amplifiers with fiber nonlinearity: the stochastic parametric noise amplification. Unlike earlier impractical approaches that compensate solely deterministic nonlinearities, here we demonstrate a novel electronic-based adaptive three-dimensional (3D) deep neural network that tackles the interplay of deterministic and stochastic nonlinearity manifestation in coherent optical signals. Our demonstration shows that 3D deep learning can compensate nonlinear inter-carrier crosstalk effects even in the presence of frequency stochastic variations, which has hitherto been considered impossible. Our solution significantly outperforms conventional 2D machine learning and gold-standard nonlinear equalizers without sacrificing computational complexity, leading to record-breaking transmission performance for up to 40 Gbit/sec high-spectral-efficient optical signals. <s> BIB035 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> A neural-network-based approach is presented to efficiently implement digital backpropagation (DBP). For a 32x100 km fiber-optic link, the resulting"learned"DBP significantly reduces the complexity compared to conventional DBP implementations. <s> BIB036 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> A new geometric shaping method is proposed, leveraging unsupervised machine learning to optimize the constellation design. The learned constellation mitigates nonlinear effects with gains up to 0.13 bit/4D when trained with a simplified fiber channel model. <s> BIB037 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <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> BIB038 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <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> BIB039 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <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> BIB040 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <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> BIB041 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Nonlinear phase noise (known as Gordon–Mollenauer phase noise) results from power fluctuations arising from amplified spontaneous-emission noise of the inline amplifier which is converted into phase fluctuations due to the self-phase modulation effect. It may severely impair the phase of optical signal, and would further degrade the performance of the long-haul optical transmission systems. The case can be compensated partially by mid-span optical phase conjugation(OPC). In this paper, an effective k-nearest neighbor (KNN) detector have been introduced to further improve the performance of OPC system by mitigating non-Gaussian impairments caused by nonlinear phase noise. The effectiveness is investigated in a 112-Gb/s 16QAM optical phase conjugation system with 800-km dispersion-managed link and 1280-km dispersion-shifted-fiber link. The results show that the effective and optimal KNN detector can enhance the overall performance of OPC system, even the asymmetric system. <s> BIB042 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We experimentally demonstrate a low-complexity and zero-redundancy fiber nonlinearity mitigation technique in the 16-QAM and 64-QAM coherent optical communication systems based on the non-data-aided (ND) k -nearest neighbors (KNN) algorithm. We measured the bit error rate (BER) performances of the 16-QAM and 64-QAM signals in the back-to-back case and in the single-mode-fiber transmission case and achieved notable BER improvement by the ND-KNN technique. The ND-KNN technique enables us to compensate any nondeterministic transmission impairments and does not require any extra training data, which is powerful to mitigate the fiber nonlinearity impairments in the 16-QAM and 64-QAM coherent optical communication systems. By utilizing the proposed ND-KNN method, we achieved 0.5-dB BER improvement in the 800-km single-mode fiber (SMF) 16-QAM transmission system and approximately 2-dB BER improvement in the 80-km SMF 64-QAM transmission system. The algorithm first utilizes the density parameter of the testing data to extract rapidly the center noiseless data and label them as the classification references and then applies the KNN method to classify the remaining testing data. Therefore, the algorithm is robust to the system noise and can achieve fast convergence. The proposed ND-KNN equalization technique can provide efficient compensation at a low computation cost and zero data redundancy and is promising for real-world application. <s> BIB043 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Predicting the quality of transmission (QoT) of a lightpath prior to its deployment is a step of capital importance for an optimized design of optical networks. Due to the continuous advances in optical transmission, the number of design parameters available to system engineers (e.g., modulation formats, baud rate, code rate, etc.) is growing dramatically, thus significantly increasing the alternative scenarios for lightpath deployment. As of today, existing (pre-deployment) estimation techniques for light-path QoT belong to two categories: “exact” analytical models estimating physical-layer impairments, which provide accurate results but incur heavy computational requirements, and margined formulas, which are computationally faster but typically introduce high link margins that lead to underutilization of network resources. In this paper, we explore a third option, i.e., machine learning (ML), as ML techniques have already been successfully applied for optimization and performance prediction of complex systems where analytical models are hard to derive and/ or numerical procedures impose high computational burden. We investigate a ML classifier that predicts whether the bit error rate of unestablished lightpaths meets the required system threshold based on traffic volume, desired route, and modulation format. The classifier is trained and tested on synthetic data and its performance is assessed over different network topologies and for various combinations of classification features. Results in terms of classifier accuracy are promising and motivate further investigation over real field data. <s> BIB044 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Abstract This paper presents a new approach when dealing with availability and reliability of wireless optics by employing several prediction methods for RSSI parameter in hard FSO/RF switching methodology. Despite the fact that FSO link signal and its overall communication is strongly affected by weather conditions (fog, heavy rains, snow, etc.), the strength of RSSI will finally result in link availability or a signal fade. Eventually, there is almost unlimited number of weather states which have either positive or negative impact on RSSI and FSO link, respectively. However, the way that nature and surrounding world are organized is behavioral patterns which can be found everywhere. Finding appropriate pattern is mostly matter of scale of observed group of parameters. Machine learning, data mining or big data are interchangeable terms used in pattern finding processes. We have decided to leverage the power of machine learning algorithms to predict RSSI parameter base on measured weather parameters in this paper. First chapter is dedicated to a deep analysis of negative effects encountered when light propagates through an atmospheric channel and deteriorate optical signal. In following, we paid attention to practical design and assembly of a monitoring device. The further discussion evaluates the methodologies of so called soft and hard switching between FSO and RF (Radio Frequency) link. The primary attention was paid to the hard switching technique and to relations between the received signal strength indicator with the parameters influencing atmospheric channel. Selected machine learning methods and their modifications (AdaBoostClassifier with DecisionTreeClassifier, RandomForestClassifier, GradientBoostingClassifier) were applied in this analysis in order to estimate received optical power parameter based on series of weather parameters. Such a process involves classification and regression methods. This paper presents finding out an optimal scheme for input matrix as an essential for machine learning training process. Considered time and calculation requirements are compensated by employing automation tools in overall process. Finally, we conclude and evaluate the outcomes of machine learning models in FSO/RF hard switching process. <s> BIB045 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Using monitored physical parameters in a learning process, we decrease design margins by reducing uncertainties on the input parameters of a Quality of Transmission (QoT) tool, improving the accuracy of the signal-to-noise ratio prediction. <s> BIB046 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Flexgrid optical networking relies on spectrum defragmentation to groom channel wavelengths and improve spectral efficiency. Various defragmentation methods—hop, make-before-break, and sweep—interact with the power dynamics of erbium-doped fiber amplifiers (EDFA) differently and result in undesired power excursions that exacerbate the post-EDFA power variance. We present a machine learning engine that characterizes the channel dependence of power excursions from historical data. We further demonstrate that post-EDFA power variance during hop, make-before-break, and sweep defragmentation methods can be greatly mitigated by the trained machine learning engine with automated and expedited power adjustment predictions. <s> BIB047 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We propose a spectrally efficient digitized radio-over-fiber (D-RoF) system by grouping highly correlated neighboring samples of the analog signals into multidimensional vectors, where the k-means clustering algorithm is adopted for adaptive quantization. A 30 Gbit/s D-RoF system is experimentally demonstrated to validate the proposed scheme, reporting a carrier aggregation of up to 40 100 MHz orthogonal frequency division multiplexing (OFDM) channels with quadrate amplitude modulation (QAM) order of 4 and an aggregation of 10 100 MHz OFDM channels with a QAM order of 16384. The equivalent common public radio interface rates from 37 to 150 Gbit/s are supported. Besides, the error vector magnitude (EVM) of 8% is achieved with the number of quantization bits of 4, and the EVM can be further reduced to 1% by increasing the number of quantization bits to 7. Compared with conventional pulse coding modulation-based D-RoF systems, the proposed D-RoF system improves the signal-to-noise-ratio up to ∼9 dB and greatly reduces the EVM, given the same number of quantization bits. <s> BIB048 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> For the converged use of LTE, WLAN, and visible light communication in indoor scenarios, fine-grained and intelligent network selection is essential for ensuring high user quality of experience. To tackle the challenges associated with dynamic environments and complicated service requirements, we propose a context-aware solution for indoor network selection. Specifically, three-level contextual information is revealed and exploited in both the utility and algorithm designs. In particular, the contextual information about the asymmetric downlink-uplink features of network performance is used to design a fine-grained utility model. A context-aware learning algorithm sensitive to traffic type-location-time information is proposed. The time-location dependent periodic changing rule of load statistical distributions is further used to realize efficient online network selection via knowledge transfer. The simulation results show that the proposed algorithm can achieve much better performance with faster convergence speed than traditional reinforcement learning. <s> BIB049 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We propose a novel artificial intelligence (AI) assisted framework to realize virtual network (VNT) slicing in an inter-datacenter optical network (IDCON), where the infrastructure provider (InP) performs resource advertising and pricing based on deep reinforcement learning (DRL) and grants the virtual network embedding (VNE) schemes calculated distributedly by the tenants. Simulation results confirm that compared with the traditional centralized VNT slicing framework, our proposal can not only make the InP more profitable but also relieve its computation complexity effectively. <s> BIB050 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Abstract Optimum Multiple Input Multiple Output (MIMO) detector has always been a challenge in MIMO communication systems. In this paper, a novel MIMO detector has been designed using a supervised Deep Learning Neural Network (DLNN) and has been implemented successfully in a Mode Division Multiplexing (MDM) optical transmission system. A conventional Graded-Index Multi-Mode Fiber (GI-MMF) is used to design an MDM optical transmission system. We have used a DLNN for MIMO detection in MDM optical transmission system and have compared its performance with Zero Forcing (ZF) detector and Semi-Definite Relaxation Row-by-Row (SDR-RBR). The results confirm that our DLNN outruns the performance of traditional MIMO detectors. <s> BIB051 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Abstract As network traffic is expected to continue to grow at high rates for the foreseeable future, it becomes imperative to introduce space division multiplexing elastic optical networks (SDM-EONs) into the optical transport network. However, spectrum fragmentation and crosstalk present significant challenges that may negatively impact the performance of SDM-EONs. In this paper, we leverage machine learning techniques to enhance the transmission performance of SDM-EONs, and make two contributions. Specifically, we use an Elman neural network to forecast traffic demands, and use a two-dimensional rectangular packing model to allocate spectrum so as to decrease unnecessary spectrum fragmentation (and, in turn, increase resource utilization). We also present a novel spectrum partition scheme to reduce crosstalk. Our evaluation study confirms that the proposed strategy is effective in improving spectrum utilization while reducing blocking probability and crosstalk. <s> BIB052 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We propose an autoencoding sequence-based transceiver for communication over dispersive channels with intensity modulation and direct detection (IM/DD), designed as a bidirectional deep recurrent neural network (BRNN). The receiver uses a sliding window technique to allow for efficient data stream estimation. We find that this sliding window BRNN (SBRNN), based on end-to-end deep learning of the communication system, achieves a significant bit-error-rate reduction at all examined distances in comparison to previous block-based autoencoders implemented as feed-forward neural networks (FFNNs), leading to an increase of the transmission distance. We also compare the end-to-end SBRNN with a state-of-the-art IM/DD solution based on two level pulse amplitude modulation with an FFNN receiver, simultaneously processing multiple received symbols and approximating nonlinear Volterra equalization. Our results show that the SBRNN outperforms such systems at both 42 and 84 Gb/s, while training fewer parameters. Our novel SBRNN design aims at tailoring the end-to-end deep learning-based systems for communication over nonlinear channels with memory, such as the optical IM/DD fiber channel. <s> BIB053 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Optical signal-to-noise ratio (OSNR) monitoring is essential to both the operation of reliable and reconfigurable network and the supply of high quality-of-service. Recently, deep learning technique has been proposed for the implementation of OSNR monitoring. However, the potential of deep learning technique for OSNR monitoring has not been fully exploited in terms of the monitoring accuracy and robust operation. Here, we propose an OSNR monitoring scheme with high accuracy and short response time using the long short-term memory neural network (LSTM-NN). The use of LSTM-NN is helpful to identify the relationship between the time-varied data and corresponding OSNR without manual feature extraction. We investigate the optimal number of time steps for the LSTM-NN-based OSNR monitoring and the monitoring accuracy with respect to various modulation formats with variable baud-rates. Finally, we implement an experimental verification for 34.94-GBd polarization division multiplexing-16 quadrature amplitude modulation signal, achieving a mean absolute error of 0.05 dB over the OSNR range from 15 to 25 dB. The advantages of LSTM-NN-based OSNR monitoring include high accuracy and smart response, which is ideal for future agile optical network. <s> BIB054 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> A multi-layer neural network is employed to learn the mapping between Raman gain profile and pump powers and wavelengths. The learned model predicts with high-accuracy, low-latency and low-complexity the pumping setup for any gain profile. <s> BIB055 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We first propose a novel multi-domain routing paradigm that transforms the routing problem from heuristic-algorithm-based computation to artificial-intelligence-based data analytics. Numerical results prove that our proposal can achieve excellent routing accuracy, and significant signaling reduction. <s> BIB056 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <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> BIB057 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <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> BIB058 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <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> BIB059 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <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> BIB060 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> WLAN-based indoor positioning algorithm has the characteristics of simple layout and low price, and it has gradually become a hotspot in both academia and industry. However, due to the poor stability of Wi-Fi signals, received signal strength (RSS) fingerprints of some adjacent reference positions are difficult to evaluate similarity when utilizing traditional distance-based calculation methods. By clustering these RSS fingerprints into one region, the commonly utilized KNN algorithm in the past cannot achieve accurate positioning in the region. For this, we introduce a concept of the insensitive region of the RSS fingerprint and a new algorithm named DBSCAN-KRF. This algorithm can delete noise sample and detect insensitive region, then, different methods are selected to achieve indoor positioning by judging the region of the estimated fingerprint sample, the KNN algorithm is selected when the region is sensitive, and random forest algorithm is selected when the region is insensitive. The experimental results show that the DBSCAN-KRF algorithm is superior while compared with other alternative indoor positioning algorithms. <s> BIB061 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Coherent optical multi-carrier communications have recently dominated metro-regional and long-haul optical communications. However, the major obstacle of networks involving coherent multi-carrier signals such as coherent optical orthogonal frequency-division multiplexing (CO-OFDM) is the fiber-induced nonlinearity and the parametric noise amplification from cascaded optical amplifiers which results in significant nonlinear distortion among subcarriers. Here, we present the first nonlinear equalizer in optical communications using the traditional Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a novel modified version of DBSCAN which combines K-means clustering on the noisy un-clustered symbols. For a 24.72 Gbit/sec differential quaternary phase-shift keying (DQPSK) CO-OFDM system, the modified DBSCAN can increase the signal quality-factor by up to 2.158 dB compared to linear equalization at 500 km of transmission. The modified DBSCAN slightly outperforms the traditional DBSCAN, fuzzy-logic C-means, hierarchical and conventional K-means clustering at high launched optical powers. <s> BIB062 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> We investigate the performance of a machine learning classification technique, called the Parzen window, to mitigate the fiber nonlinearity in the context of dispersion managed and dispersion unmanaged systems. The technique is applied for detection at the receiver side and deals with the non-Gaussian nonlinear effects by designing improved decision boundaries. We also propose a two-stage mitigation technique using digital back propagation and Parzen window for dispersion unmanaged systems. In this case, digital back propagation compensates for the deterministic nonlinearity and the Parzen window deals with the stochastic nonlinear signal–noise interactions, which are not taken into account by digital back propagation. A performance improvement up to 0.4 dB in terms of Q factor is observed. <s> BIB063 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Abstract With more and more human underwater activities carrying out, underwater visible light communication has received increasing attention worldwide. To solve the problem of noise and nonlinearity in underwater visible light communication systems, we proposed a phase deviation correction algorithm based on K-means clustering to correct the phase deviation of special-shaped 8-QAM constellations. The experimental results indicate the performance of the circle has the maximum improvement after using the phase deviation correction algorithm. Before the use of the K-means phase correction algorithm, the highest data rate that circle can achieve is 1.3875 Gbps. The highest data rate increases to 1.4625 Gbps after applying the algorithm. <s> BIB064 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> Amplitude jitter is a common phenomenon in pulse amplitude modulation (PAM) visible light communication (VLC) systems, which greatly affects the system performance. In this paper, we propose a novel time-amplitude two-dimensional re-estimation based on density-based spatial clustering of applications with noise (DBSCAN) algorithm of machine learning (2DDB) to distinguish different signal levels with jitter. The Q factor of a PAM-8 VLC system applying 2DDB is improved by 1.6–3.2 dB through experimental demonstration. In addition, we also investigate the influence of amplitude jitter with different levels on PAM-8 systems, and analyze the range of parameters and applications of a DBSCAN algorithm. To the best of our knowledge, this is the first time that DBSCAN of machine learning is successfully applied to PAM VLC systems. <s> BIB065 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> The rise of Industry 4.0 is pushing more machine devices into the connection of the communication network. Internet of Things (IoT) emerges as an enabling technique to support automatic information transmission and the universal interconnection among massive machine devices. Unlike the human type communication (HTC) in the conventional Internet, IoT features machine type communication (HTC) with small data granularity and massive connections, which impose new challenges on the network resource allocation earlier designed for HTC communication. One of the challenging issues is how we can manage the network resource for the IoT service provisioning. As a promising network architecture for the “last-mile” access in 5G communication, Fiber-Wireless (FiWi) access network compromises the high bandwidth capacity of the optical access network and the flexibility of wireless access network. Especially, network virtualization as an emerging technique provides FiWi with the feasibility for the coordinated bandwidth allocation of wireless access network and optical access network. The evolution of FiWi access network should be not only oriented to the technological breakthrough in regular traffic, but also be supportive of the emerging IoT traffic. In this paper, we focus on the resource allocation problem in FiWi access network supporting IoT service. We first analyze the dynamics of regular traffic and propose the traffic prediction method based on Q-Learning. Then, we propose a virtual network embedding algorithm to map exactly the virtual networks of IoT traffic to the idle resource of the virtual networks of regular traffic without degrading the performance of regular traffic. Thus, the proposed algorithm can improve the resource utilization more effectively. The simulation results demonstrate that the proposed algorithm can achieve a higher acceptance ratio and lower migration ratio of IoT traffic. <s> BIB066 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s>  <s> As the services in elastic optical networks (EONs) are bandwidth-intensive, unpredictable and dynamic, increasing network factors are emerging to affect the performance of network survivable planning and operation from network capacity to efficiency. Most of the traditional protection and restoration approaches may become before long inefficient due to the improvement of a particular network performance metric always at the expense of others. We argue that it would be more beneficial for comprehensive optimization of network performance to consider main network metrics jointly. Moreover, the highly dynamic features of EONs call for the new generation of machine learning-based solutions that are flexible and adaptable to cope with the dynamic nature of services to perform analytics. In this paper, we investigate the problem of global optimization of network performance under survivable EON environment. Specifically, a criterion, named the whole network cost-effectiveness value with survivability (WCES), is defined to measure the overall network performance by balancing the interaction among main network metrics. Then we propose a deep reinforcement learning (DRL) -based heuristic with the objective of improving overall network performance, in which two agents are utilized to provide working and protection schemes converging toward better survivable routing, modulation level and spectrum assignment (S-RMLSA) policies. Numerical results show that the proposed criterion can efficiently measure the overall network performance, and the double-agent DRL-based heuristic can greatly improve WCES while ensuring the network survivability and paying the acceptable extra consumption of request blocking probability. <s> BIB067
Network (ANN) , Deep (DNN)   BIB035 BIB036 BIB051 BIB052 BIB021 BIB026  BIB053  BIB037 [85] [86] BIB054 BIB055 BIB056 , and Convolutional Neural Network (CNN) BIB038 BIB027 BIB039 BIB057 BIB058 BIB040 BIB041 BIB059 BIB060 , k-Nearest Neighbors (KNN) BIB061 BIB042 BIB022 BIB043 , Random Forest BIB028 BIB044 BIB045 , Regression BIB023 BIB029 BIB046 [109] BIB030 BIB047  , (in unsupervised category  BIB031 BIB018 BIB010 BIB032 BIB062 BIB063 BIB033 BIB048 BIB011 BIB012 BIB019 BIB064 BIB065 BIB020 BIB024 BIB016  BIB025 BIB034 :) k-means BIB032 BIB062 BIB063 BIB033 BIB048 BIB011 BIB012 BIB019 BIB064 BIB065 , Principal Component Analysis (PCA) BIB020 BIB024 BIB016  BIB025 BIB034 , (in Reinforcement Learning category BIB017 BIB003 BIB007 BIB008 BIB009 BIB004 BIB014 BIB001 BIB005  BIB046 BIB013 BIB049 BIB006 BIB015 BIB002 BIB066 BIB067 BIB050 :), Q-Learning BIB006 BIB015 BIB002 BIB066 , and Deep Reinforcement Learning BIB067 BIB050 are used in the referenced papers of this manuscript.