A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> Applications using artificial neural networks (ANNs) for optical performance monitoring (OPM) are proposed and demonstrated. Simultaneous identification of optical signal-to-noise-ratio (OSNR), chromatic dispersion (CD), and polarization-mode-dispersion (PMD) from eye-diagram parameters is shown via simulation in both 40 Gb/s on-off keying (OOK) and differential phase-shift-keying (DPSK) systems. Experimental verification is performed to simultaneously identify OSNR and CD. We then extend this technique to simultaneously identify accumulated fiber nonlinearity, OSNR, CD, and PMD from eye-diagram and eye-histogram parameters in a 3-channel 40 Gb/s DPSK wavelength-division multiplexing (WDM) system. Furthermore, we propose using this ANN approach to monitor impairment causing changes from a baseline. Simultaneous identification of accumulated fiber nonlinearity, OSNR, CD, and PMD causing changes from a baseline by use of the eye-diagram and eye-histogram parameters is obtained and high correlation coefficients are achieved with various baselines. Finally, the ANNs are also shown for simultaneous identification of in-phase/quadrature (I/Q) data misalignment and data/carver misalignment in return-to-zero differential quadrature phase shift keying (RZ-DQPSK) transmitters. <s> BIB001 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> We experimentally demonstrate the use of artificial neural networks trained with parameters derived from both delay-tap plots and eye diagrams for multi-impairment monitoring in a 40-Gbit/s non-return-to-zero on-off keying system. <s> BIB002 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> We propose an optical performance monitoring technique for simultaneous monitoring of optical signal-to-noise ratio (OSNR), chromatic dispersion (CD), and polarization-mode dispersion (PMD) using an artificial neural network trained with asynchronous amplitude histograms (AAHs). Simulations are conducted to demonstrate the technique for both 40-Gb/s return-to-zero differential quadrature phase-shift keying (RZ-DQPSK) and 40-Gb/s noneturn-to-zero 16 quadrature amplitude modulation (16-QAM) systems. The OSNR, CD, and PMD monitoring range and root-mean-square (rms) errors are 10-30 and 0.43 dB, 0-400 and 9.82 ps/nm, and 0-10 and 0.92 ps, respectively, for RZ-DQPSK systems. For 16-QAM system, the monitoring range and rms errors are 10-30 and 0.2 dB, 0-400 and 9.66 ps/nm, and 0-30 and 0.65 ps for OSNR, CD, and PMD, respectively. As the generation of AAH does not require any clock or timing recovery, the proposed technique can serve as a low-cost option to realize in-service multiparameter monitoring for the next-generation transparent optical networks. <s> BIB003 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> A technique using artificial neural networks trained with parameters derived from reconstructed eye diagrams for optical performance monitoring in 40-Gbps optical duobinary (ODB) system is demonstrated. Firstly, the optical signal is asynchronously sampled by short pulse in the nonlinear medium such as semiconductor optical amplifier and highly nonlinear fiber, the sampled and collected data is then processed by improved software synchronization algorithm to obtain reconstructed eye diagrams without data clock recovery. Secondly, the features of the reconstructed eye diagrams are extracted to train the three-layer preceptor artificial neural network. Finally, the outputs of trained neural network are used to monitor multiple optical signal impairments. Simulation experiments of optical signal noise ratio (OSNR), chromatic dispersion (CD) and polarization mode dispersion (PMD) monitoring in 40-Gbps ODB system is presented. The proposed monitoring scheme can accurately identify simultaneous impairment with the root-mean-square (RMS) monitoring error less than 3%. <s> BIB004 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> A technique using artificial neural networks trained with parameters derived from reconstructed eye diagrams for multi-impairment monitoring in a 40-Gbps RZ-OOK system is demonstrated. The proposed monitoring scheme can accurately identify simultaneous impairments without requiring data clock recovery. <s> BIB005 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> We demonstrate a technique of using artificial neural networks trained with parameters derived from asynchronous diagrams for optical performance monitoring of quadrature phase-shift-keying (QPSK) data signals. We show that asynchronous diagrams generated from balanced detection give superior results compared to single-ended detection in a 100 Gb/s QPSK system. Experimental demonstration verifies the effectiveness of the proposed technique. <s> BIB006 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> Optical neural-network circuit to process QPSK labels for photonic label routing is proposed, which consists of optical amplifiers, phase shifters and nonlinear threshold devices. Parameters in these devices are learned with back-propagation of teacher signals. <s> BIB007 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> We propose a low-cost technique for simultaneous and independent optical signal-to-noise ratio (OSNR), chromatic dispersion (CD), and polarization-mode dispersion (PMD) monitoring in 40/56-Gb/s return-to-zero differential quadrature phase-shift keying (RZ-DQPSK) and 40-Gb/s RZ-DPSK systems, using artificial neural networks (ANN) trained with empirical moments of asynchronously sampled signal amplitudes. The proposed technique employs an extremely simple hardware and digital signal processing to enable multi-impairment monitoring at different data rates and for various modulation formats without necessitating hardware changes. Simulation results demonstrate wide dynamic ranges and good monitoring accuracies. <s> BIB008 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> We propose a simple and cost-effective technique for modulation format identification (MFI) in next-generation heterogeneous fiber-optic networks using an artificial neural network (ANN) trained with the features extracted from the asynchronous amplitude histograms (AAHs). Results of numerical simulations conducted for six different widely-used modulation formats at various data rates demonstrate that the proposed technique can effectively classify all these modulation formats with an overall estimation accuracy of 99.6% and also in the presence of various link impairments. The proposed technique employs extremely simple hardware and digital signal processing (DSP) to enable MFI and can also be applied for the identification of other modulation formats at different data rates without necessitating hardware changes. <s> BIB009 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> Optical fibers are now widely used in communication systems, mainly because they offer faster data transmission speeds, compared to any other type of digital communication system. Despite this great advantage, problems remain that prevent the full exploitation of optical connection: by increasing transmission rates over longer distances, the data is affected by nonlinear inter-symbol interference caused by dispersion phenomena in the fiber. Adaptive equalizers can be used to compensate for the effects caused by nonlinear channel responses, restoring the signal originally transmitted. The present work proposes a multilayer perceptron equalizer based on artificial neural networks. The technique was validated using a simulated optical channel, and was compared with other adaptive equalization techniques. <s> BIB010 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> Visible light communications (VLCs) using white light emitting diodes have become popular because of the ability to provide illumination and data communications simultaneously. There are a number of challenges in VLC systems among which the most critical being the effect of fluorescent light interference (FLI), which is largely ignored. In this letter, we study the effect of FLI and introduce the wavelet transform and artificial neural network based receiver to reduce the effect of FLI as well as increase the available data rates. An error free data communication up to 150 Mb/s is achieved using non-return-tozero on-off keying (OOK-NRZ) modulation. This is the highest practically reported data rate for a single link using OOK-NRZ and a p-i-n photodetector. <s> BIB011 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> This paper presents the use of a digital signal processor (DSP) to implement an artificial neural network (ANN) equalizer in an organic light emitting diode (OLED) based visible light communications (VLC) system for the first time. The ANN is used to counteract the inter-symbol interference (ISI) experienced when the transmission rate exceeds the system modulation bandwidth. The link is made up of an OLED as transmitter with a silicon photodetector as receiver. The bandwidth off the OLED is 93 kHz, enforcing a low pass transfer function on the system. The bit rate achieved is 500 kb/s, which agrees well with similar systems that have been processed with MATLAB. <s> BIB012 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> This paper presents the first ever experimental demonstration of a multiple-input multiple-output (MIMO) visible light communications system employing four silicon (Si) light emitting diodes (LEDs) and four organic photodetectors (OPDs) as transmitters and receivers, respectively. The proposed link is relatively low cost and it employs the on-off keying (OOK) modulation format offering a data rate of 200 kb/s without the need for equalization, which is a significant increase compared with previous non-equalized systems. In order to speed up date rates further, we implement an artificial neural network (ANN) to classify the signal and correct the error induced by the matrix inversion at the receiver, allowing a gross bit rate of 1.8 Mb/s in the best case. <s> BIB013 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> We propose an artificial neural network (ANN) equalizer for transmission performance enhancement of coherent optical OFDM (C-OOFDM) signals. The ANN equalizer showed more efficiency in combating both chromatic dispersion (CD) and single-mode fibre (SMF)-induced non-linearities compared to the least mean square (LMS). The equalizer can offer a 1.5 dB improvement in optical signal-to-noise ratio (OSNR) compared to LMS algorithm for 40 Gbit/s C-OOFDM signals when considering only CD. It is also revealed that ANN can double the transmission distance up to 320 km of SMF compared to the case of LMS, providing a nonlinearity tolerance improvement of ∼0.7 dB OSNR. <s> BIB014 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> In this paper, we experimentally demonstrate for the first time an on off keying modulated visible light communications system achieving 170 Mb/s using an artificial neural network (ANN) based equalizer. Adaptive decision feedback (DF) and linear equalizers are also implemented and the system performances are measured using both real time (TI TMS320C6713 digital signal processing board) and offline (MATLAB) implementation of the equalizers. The performance of each equalizer is analyzed in this paper using a low bandwidth (4.5 MHz) light emitting diode (LED) as the transmitter and a large bandwidth (150 MHz) PIN photodetector as the receiver. The achievable data rates using the white spectrum are 170, 90, 40 and 20 Mb/s for ANN, DF, linear and unequalized topologies, respectively. Using a blue filter to isolate the fast blue component of the LED (at the cost of the power contribution of the yellowish wavelengths) is a popular method of improving the data rate. We further demonstrate that it is possible to sustain higher data rates from the white light with ANN equalization than the blue component due to the high signal-to-noise ratio that is obtained from retaining the yellowish wavelengths. Using the blue component we could achieve data rates of 150, 130, 90 and 70 Mb/s for the same equalizers, respectively. <s> BIB015 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> We propose a radial basis function neural network (RBFNN)-based nonlinear equalizer (NLE) for coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. The hidden layer neuron weights of the RBFNN-NLE are calculated using the $K$ -means clustering algorithm and the output layer weights are updated using the least mean square algorithm. With only 3% overhead in training, the proposed RBFNN-NLE was found to provide up to 4-dB performance improvement in terms of $Q$ -factor for 70-Gb/s 16-QAM CO-OFDM transmission over 1000 km ( $10\times 100$ km) fiber. Numerical results show that the operating data rate is 80 Gb/s at $Q = 6.25$ dB with the proposed RBFNN-NLE, compared with previously reported value of 70 Gb/s with artificial neural network-based NLE. <s> BIB016 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> Abstract A bio-inspired detector based on the artificial neural network (ANN) and genetic algorithm is proposed in the context of a coherent optical transmission system. The ANN is designed to mitigate 16-quadrature amplitude modulation system impairments, including linear impairment: Gaussian white noise, laser phase noise, in-phase/quadrature component imbalance, and nonlinear impairment: nonlinear phase. Without prior information or heuristic assumptions, the ANN, functioning as a machine learning algorithm, can learn and capture the characteristics of impairments from observed data. Numerical simulations were performed, and dispersion-shifted, dispersion-managed, and dispersion-unmanaged fiber links were investigated. The launch power dynamic range and maximum transmission distance for the bio-inspired method were 2.7 dBm and 240 km greater, respectively, than those of the maximum likelihood estimation algorithm. Moreover, the linewidth tolerance of the bio-inspired technique was 170 kHz greater than that of the k-means method, demonstrating its usability for digital signal processing in coherent systems. <s> BIB017 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> We experimentally demonstrate a 60-GHz radioover-fiber (RoF) system using an adaptive activated artificial neural network nonlinear equalizer (ANN-NLE) to improve the bit error rate (BER) performance. We propose a variable-α activation function ANN-NLE with a faster convergence speed and a stronger equalization ability to solve the nonlinear problems generated from the RoF link. To the best of our knowledge, this letter is the first to apply an adaptive activation function ANN to solve a classification problem and equalize the signals in a 60-GHz RoF system. Using the proposed ANN-NLE, a higher convergence speed and better BER performances can be achieved. Comparisons between our proposed ANN-NLE and nonlinear Volterra equalizer in polynomial structure are reported. Using the proposed ANN-NLE, the system successfully transmits a 5-Gbps BPSK signal through a 10-km fiber and 1.2-m wireless link under a forward error correction limit of 10 ::: -3 ::: with much higher power sensitivity than the Volterra equalizer. <s> BIB018 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> We propose a cognitive scalable method based on neural networks to address dynamic and agile provisioning of optical physical layer without prior knowledge of network specifications. Experimental demonstrations on a mesh network achieve 90th percentile OSNR prediction of 0.25dB Root-Mean-Squared-Error. <s> BIB019 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> We investigate the risk of overestimating the performance gain when applying neural network-based receivers in systems with pseudorandom bit sequences or with limited memory depths, resulting in repeated short patterns. We show that with such sequences, a large artificial gain can be obtained, which comes from pattern prediction rather than predicting or compensating the studied channel/phenomena. <s> BIB020 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> Abstract The sensor-less adaptive optics (AO) is one of the most promising methods to compensate strong wave front disturbance in free space optics communication (FSO). The back propagation (BP) artificial neural network is applied for the sensor-less AO system to design a distortion correction scheme in this study. This method only needs one or a few online measurements to correct the wave front distortion compared with other model-based approaches, by which the real-time capacity of the system is enhanced and the Strehl Ratio (SR) is largely improved. Necessary comparisons in numerical simulation with other model-based and model-free correction methods proposed in Refs. [6] , [8] , [9] , [10] are given to show the validity and advantage of the proposed method. <s> BIB021 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> Recently, the neural network approach for the blocking probability evaluation on optical networks was proposed, in which the inputs of the neural network were the optical network parameters and the output was the blocking probability of the optical network. The numerical results showed that its evaluation speed of the blocking probability was thousands of times faster than that of the discrete event simulator. However, the existing approach had two drawbacks. First, when the blocking probability was small, there was a significant approximation error due to the high dynamic range of the blocking probability. Second, the single-hidden-layer feedforward network model was used, which needed some time-consuming training algorithms to learn the parameters of hidden nodes, such as backpropagation. To solve these problems, this paper proposes to use the mean squared error of the log blocking probability as the training objective and use the extreme learning machine (ELM) framework for the training. Our numerical results show that the blocking probability estimated by our training objective is much more accurate than that of the existing approach, and it is obtained efficiently due to the greatly simplified training procedure offered by the ELM. <s> BIB022 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> Machine learning techniques have proven very efficient in assorted classification tasks. Nevertheless, processing time-dependent high-speed signals can turn into an extremely challenging task, especially when the signals have been nonlinearly distorted. Recently, analogue hardware concepts using nonlinear transient responses have been gaining significant interest for fast information processing. Here, we introduce a simplified photonic reservoir computing scheme for data classification of severely distorted optical communication signals after extended fibre transmission. To this end, we convert the direct bit detection process into a pattern recognition problem. Using an experimental implementation of our photonic reservoir computer, we demonstrate an improvement in bit-error-rate by of two orders of magnitude, compared to directly classifying the transmitted signal. This improvement corresponds to an extension of the communication range by over 75%. While we do not yet reach full real-time post-processing at telecom rates, we discuss how future designs might close the gap. <s> BIB023 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> We first analyze the nonlinearities in the analog radio-over-fiber (A-RoF)-based mobile fronthaul systems with a multi-user uplink load. The inter-band cross modulations (XMs) present in the common fiber channel cause severe inter-user interference. Then, we propose a novel complex-valued multi-level artificial neural network nonlinear equalizer (ANN-NLE) for single-carrier frequency division multiple access signal uplink transmissions. The inter-user interference can be mitigated by employing a multi-user ANN-NLE. Finally, we design an experimental test bed comprising a 60-GHz wireless link and an A-RoF link in order to verify the model predictions and thereby demonstrate the ability of the ANN-NLE to overcome the nonlinear intra-band and inter-band XMs in the case of simultaneous joint equalizations of all users (multi-point-to-single-point) as well as in the case of individual user equalizations (point-to-point). From the experimental and simulation results, we demonstrate that the ANN-NLE can substantially reduce the inter-user interference and communication bit error. <s> BIB024 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> Operators are pressured to maximize the achieved capacity over deployed links. This can be obtained by operating in the weakly nonlinear regime, requiring a precise understanding of the transmission conditions. Ideally, optical transponders should be capable of estimating the regime of operation from the received signal and feeding that information to the upper management layers to optimize the transmission characteristics; however, this estimation is challenging. This paper addresses this problem by estimating the linear and nonlinear signal-to-noise ratio (SNR) from the received signal. This estimation is performed by obtaining features of two distinct effects: nonlinear phase noise and second-order statistical moments. A small neural network is trained to estimate the SNRs from the extracted features. Over extensive simulations covering 19,800 sets of realistic fiber transmissions, we verified the accuracy of the proposed techniques. Employing both approaches simultaneously gave measured performances of 0.04 and 0.20 dB of standard error for the linear and nonlinear SNRs, respectively. <s> BIB025 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> In face of staggering traffic growth driven by cloud-based platforms, modern optical networks—forming the backbone of such connectivity—are faced with increasing requirements in terms of operational reliability. The challenge is that of cognition-driven learning and fault management workflows, cost-effectively assuring the next-generation networks. Machine learning, an artificial intelligence tool, can be conceived as an extremely promising instrument to address network assurance via dynamic data-driven operation, as opposed to static pre-engineered solutions. In this paper, we propose and demonstrate a cognitive fault detection architecture for intelligent network assurance. We introduce the concept of cognitive fault management, elaborate on its integration in transport software defined network controller, and demonstrate its operation based on real-world fault examples. Our framework both detects and identifies significant faults, and outperforms conventional fixed threshold-triggered operations, both in terms of detection accuracy and proactive reaction time. <s> BIB026 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> Abstract We investigate the capacity improvement achieved by bandwidth variable transceivers (BVT) in meshed optical networks with cascaded ROADM filtering at fixed channel spacing, and then propose an artificial neural network (ANN)-aided provisioning scheme to select optimal symbol rate and modulation format for the BVTs in this scenario. Compared with a fixed symbol rate transceiver with standard QAMs, it is shown by both experiments and simulations that BVTs can increase the average capacity by more than 17%. The ANN-aided BVT provisioning method uses parameters monitored from a coherent receiver and then employs a trained ANN to transform these parameters into the desired configuration. It is verified by simulation that the BVT with the proposed provisioning method can approach the upper limit of the system capacity obtained by brute-force search under various degrees of flexibilities. <s> BIB027 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> Abstract The increased data transfers and rapidly evolving cloud services lead to the inevitable need for the new techniques applied to communication networks, such as AI, machine learning, and data analysis. In this paper, we present two approaches that employ the machine learning techniques to enable traffic prediction in Elastic Optical Networks. Results show that the application of adaptive strategies has superior performance, which is a future opportunity for telecommunication operators to improve the efficiency of their network architectures. <s> BIB028 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> In this work, we present a complete rethink of the decision-making process in allocating bandwidth in a heterogeneous Fiber-Wireless network with machine intelligence. We highlight the use of an artificial neural network (ANN) at the central office to learn the uplink latency performance using multiple network and packet features. In turn, the trained ANN enables the central office to facilitate flexible bandwidth allocations under diverse network scenarios in meeting low-latency communication demands. <s> BIB029 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> A machine learning (ML)-based multifunctional optical spectrum analysis technique is proposed to perform not only the conventional analysis functions but also the extended analysis functions, including center wavelength detection, optical signal-to-noise (OSNR) calculation, bandwidth recognition, as well as spectral distortion diagnosis. We have investigated four widely used ML algorithms, including support vector machine (SVM), artificial neural network, k-nearest neighbors, and decision tree. First, the wavelengths, OSNRs, and bandwidths of optical signals are processed by four ML methods based on the spectral data. The good performance and fast processing speed are obtained, especially for SVM, achieving the optimal accuracy (100%) and the least test time. For the need of the practical application, we also investigate the more complicated case, where wavelength, OSNR, and bandwidth are variable simultaneously so that the ML should analyze these three parameters comprehensively. Even in this case, the overall accuracy is still larger than 99.1%. In addition, the extended analysis functions are also studied to diagnose the spectral distortion caused by the cascaded filtering effect and off-center filtering effect. The number of cascaded filters and the offsets of filter shift and laser drift can be effectively estimated by the SVM with high average accuracy and low standard deviation, which are useful for failure detection and distortion recovery. This technique has the potential to be applied in the optical spectrum analyzer to implement the multifunctional spectrum analysis or in the optical performance monitor to execute the spectral distortion diagnosis. <s> BIB030 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> Fiber nonlinear interference (NLI) modeling and monitoring are the key building blocks to support elastic optical networks. In the past, they were normally developed and investigated separately. Moreover, the accuracy of the previously proposed methods still needs to be improved for heterogenous dynamic optical networks. In this paper, we present the application of machine learning (ML) in NLI modeling and monitoring. In particular, we first propose to use ML approaches to calibrate the errors of current fiber nonlinearity models. The Gaussian-noise model is used as an illustrative example, and significant improvement is demonstrated with the aid of an artificial neural network. Further, we propose to use ML to combine the modeling and monitoring schemes for a better estimation of NLI variance. Extensive simulations with 2411 links are conducted to evaluate and analyze the performance of various schemes, and the superior performance of the ML-aided combination of modeling and monitoring is demonstrated. <s> BIB031 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> In multi-domain elastic optical networks with alien wavelengths, each domain needs to consider intradomain and interdomain alien traffic to estimate and guarantee the required quality of transmission (QoT) for each lightpath and perform provisioning operations. This paper experimentally demonstrates an alien wavelength performance monitoring technique and machine-learning-aided QoT estimation for lightpath provisioning of intradomain/ interdomain traffic. Testbed experiments demonstrate modulation format recognition, QoT monitoring, and cognitive routing for a 160 Gbaud alien multi-wavelength light- path. By using experimental training datasets from the testbed and an artificial neural network, we demonstrated an accurate optical-signal-to-noise ratio prediction with an accuracy of ~95% when using 1200 data points. <s> BIB032 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> We study the effectiveness of various machine learning techniques, including artificial neural networks, support vector machine, logistic regression, K-nearest neighbors, decision tree and Naive Bayesian, for detecting and mitigating power jamming attacks in optical networks. Our study shows that artificial neural network is the most accurate in detecting out-of-band power jamming attacks in optical networks. To further mitigating the power jamming attacks, we apply a new resource reallocation scheme that utilizes the statistical information of attack detection accuracy, and propose a resource reallocation algorithm to lower the probability of successful jamming of lightpaths. Simulation results show that higher the accuracy of detection, lower is the likelihood of jamming a lightpath. <s> BIB033 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Artificial Neuronal Network <s> Abstract This work concerns receiver design for light-emitting diode (LED) multiple input multiple output (MIMO) communications where the LED nonlinearity can severely degrade the performance of communications. We firstly propose an extreme learning machine (ELM) based receiver to jointly handle the LED nonlinearity and cross-LED interference. Then, by taking advantage of the features of the ELM, we propose to use a circulant structure for the input weight matrix and the fast Fourier transform (FFT) for implementation, leading to significant computational complexity reduction. It is demonstrated that, the proposed ELM based receivers can handle the nonlinearity and interference much more effectively compared to conventional techniques, and the low complexity ELM-based receiver with circulant input matrix delivers almost the same performance as the receiver based on the conventional ELM. <s> BIB034
Definition: An ANN is a member of NN community, which its idea is taken after the working of neurons in the human brain ( Fig.8.a) . ANN is a variety of ML which comes under the shelf of Artificial Intelligence. ANN, due to its results, is one of the mostly used ML algorithms in OC applications. The basic idea of ANN is taken from brain neural networks, a collection of some neurons (artificial neurons) that are connected to gather. At each connection (neuron) input signals from other neurons are processed and fired by a non-linear function of the sum of input signals. Each connection link has a weight that adjusts while training. The received summed signal at each neuron is compared with a threshold and sent only while aggregating it (Fig.8.b ). ANN is composed of interconnected nonlinear activation units that take multiple inputs and create single output. The input = [ 1 , 2 , … , ], weight = [ 1 , 2 , … ], bias , as well as activation function (. ) are parameters required for calculating the output of a single neuron. If the output is greater than a threshold, then the neuron fires and its output becomes as follows: Simplifying the above function by considering 0 = −1, and 0 = , (4) becomes as: This procedure is repeated at next (output) layer for each neuron, the aim is adjusting the weight vector and bias such that the transmitted symbol and the output of the ANN be the same. For this purpose a loss function should be defined as: where is the Batch size, and ̂ are target and output of the ANN, respectively. Adjusting the weights of an ANN requires solving the optimization problem that is not linear or polynomial; so, it cannot be solved precisely and must be approximated. This could be done by iterative algorithms such as gradient descent methods. However, heuristic algorithms such as GA, due to the complete search result in better solutions BIB017 . Furthermore, some techniques linearize the constraints and objective functions at a specific point by derivation and partial derivation for some cases  . Considering . the iterative methods, the process will be stated from an initial point and iteratively declares the updated weight vector. The weight vector updates steepest descent direction of the loss function, which is the derivation direction. In sum, the weight updating formulation becomes as follows: where is the learning rate (step size) BIB017 . The ANN is a framework of ML that derives complicated relationships between input and target. It learns a specific task by using the label and features without being manually programmed with rules. For example, in bit detection, ANN learns to identify a received bit of data to be 0 or 1 by using example bits that are manually labeled as "0" or "1"; then, the learned ANN could easily identify other bits without prior information by just identifying relationship between input and output. The idea of ANN could be implemented in both electrical and optical domains. In electrical domain, the implementation is the standard ANN implementation. However, ANN could be practically implemented in optical framework (for processing QPSK labels to rout photonic label) BIB007 . In this framework, neurons are optical amplifier and phase shifter, activation functions are optical nonlinear threshold devices, and connections are optical waveguides. ANN is proven to be very efficient as a classifier. Processing of time-dependent high-speed signals is hard, especially while considering nonlinearly. In these situations, an optical ANN, in which detection is converted to pattern recognition, could help classification of distorted signals BIB023 . Fiber OC: ANN is able to derive complex nonlinear relationships between output and target, therefore, it would be better to be used for complex tasks such as equalization. The same as SVM, most of ANN implementations are related to Fiber OC, ANN NLE could effectively combat fiber effects discussed in the last section  . The ANN is a dead body that takes breath by minimizing a loss function (adjusting its weights) using iterative algorithms, or K-means clustering, or least mean square algorithms (generally, iterative algorithms is mostly used for this aim) BIB016 . The ANN NLE has a very wide implementation scenarios in ML for OC investigations, in the following, these structures are briefly reviewed. The first issue to be considered in ANN NLE (or any other ANN application) is selecting its hyperparameters, which include number and length of training vector, number of neurons, and layer type [39] . However, these hyperparameters should be adjusted precisely, in order to avoid overfitting ( Fig.9 .c), and have a better performance. The layer type is completely dependent on the linearity or nonlinearity of the input data. For example, RBN ( Fig.9 .a) BIB016 is proper for linear data input, or MLP ( Fig.9 .b) BIB010 is proper for nonlinear data. For equalizing a channel with unstable parameters common ANN is not proper; therefore, it is better to use adaptive ANN (e.g., bi-dimensional equalization with backpropagation MLP uses two MLPs to equalize in phase and quadrature of modulated signal to implement an adaptive equalizer). Some adaptive ANNs use an activation function with a variable parameter; it is proved that this technique has a faster convergence and results in better equalization BIB018 . The inter-band cross phase modulations between users BIB024 as well as inter-user interference BIB014 could severely degrade the performance of multi-user OC systems. However, it is shown that a complex valued multi-level ANN could solve this issue BIB024 , and has better performance compared with the least mean square (LMS) BIB014 , and IVSTF [44] . The second important ANN implementation scenario is something that is not investigated in SVM at all, the optical performance monitoring. Estimation maximization of the optical link capacity can be obtained in weak nonlinearity, which requires link conditions, e.g. the linear and nonlinear optical signal to noise ratio (OSNR) BIB025 BIB019 . ANN is a good choice even for performance monitoring of OC systems, without requirement to prior information of the link. Because, ANN is able to model any relationship BIB001 . The results of performance monitoring could be used in designing optical system and device. One of the most important things that could be seen in papers of this subject is the wide range of the feature extraction techniques. Actually, extracting features is one of the most important things in ML. Higher order modulations greatly suffer from NLPN, so one of the choices for feature extraction could be the NLPN itself, or second-order statistical moments. This method has high accuracy, reduces provisioning margins, and increases resource utilization, and due to its quick response (in OSNR monitoring) is a good choice in real-time applications. The other choices (with high dynamic range and accuracy) for extracting features include the eye diagram BIB001 BIB004 BIB005 , delay-tap plots BIB002 , asynchronously sampled signal amplitudes BIB008 , asynchronous amplitude histograms (AAH) BIB003 , and asynchronous diagrams BIB006 (in joint monitoring of OSNR, chromatic dispersion (CD), and polarization mode dispersion (PMD)). Multi-parameter monitoring based on these features is cheap, and does not need synchronous sampling, clock or timing recovery. It is shown that asynchronous diagrams of balanced detection have better results than single-ended detection BIB006 BIB020 . Features of AHHs are also effective for modulation format identification (MFI) in heterogeneous Fiber OC networks BIB009 . Most of the presented works in joint monitoring are inn ANN technique; however, BIB030 used ANN, k-NN, and decision tree to process wavelengths, OSNRs, and bandwidths of optical signals based on the spectral data. For the need of the practical application, it also investigated variable wavelength, OSNR, and bandwidth. Generally speaking, ANN has more applications than SVM (because it has nonlinear nature); one of the other applications of ANN is adjusting the optical amplification gain. The adjustment of the amplification gain is very important, because it can cause ASE noise and nonlinearity. Solving this issue using the adaptive adjustment techniques becomes somehow hard while considering some constraints on this problem, such as power masks makes problem. Whereas using MLP (because of non-linear nature of this problem) could well solve it, and smoothly adjust the gain of each amplifier [57] . Network: Now, its turn to review the optical networks applications of ANN; these works are completely Fiber OC based, future works may investigate the ML for WOC networks. Modern OC networks are served as the backbone of cloudbased platforms and are encountered with increase in traffic and reliability requirements. One of their challenges is the cost-effectively of cognition driven learning and fault management techniques. ANN is appropriate for addressing network assurance via dynamic data-driven operation, as opposed to static pre-engineered solutions BIB026 . Monitoring and modeling of Fiber nonlinearity are important in elastic optical networks (EON)s. However, conventional methods do not provide high accuracy for this task. As another drawback, multi-domain EONs with alien wavelengths, the intra-domain and interdomain alien traffic should be monitored for estimating QoT of each lightpath and applying provisioning operations. These issue are considered in the existing literature in ML for OC, e.g. BIB031 used a dual-functional ANN to first calibrate the deviations of the existing models, and to combine link modeling and monitoring for better estimating variance of fiber nonlinear noise. Furthermore, BIB032 developed an alien wavelength performance monitoring technique and ANN-based lightpath QoT estimation. An ANN-based OSNR monitoring of an OC network is presented in  , which in a Software Defined Network, a monitoring database decides configuring a probabilistic shaping based bandwidth variable transmitter and increases capacity by adapting the spectral efficiency. It is proven that use of ANN for selecting optimal symbol rate and modulation format in bandwidth variable transceivers in meshed optical networks with cascaded ROADM filtering at fixed channel spacing has higher average capacity compared with a fixed symbol rate transceiver with standard QAMs BIB027 . As a final note, a comprehensive investigation is done in BIB033 , and various ML techniques, including ANN, SVM, logistic regression, K-nearest neighbors, decision tree and Naive Bayesian are compared in applications such as attack detection accuracy, true positive rate and true negative rate. Further, it is proven that ANN is the most accurate in detecting out of band power jamming attacks in optical networks. ANN is able to predict network traffic BIB028 , and reduces latency as well as learning uplink latency and achieving flexible bandwidth allocation BIB029 . Using ANN, the flexible bandwidth allocations can be achieved, which leads to low-latency communication. WOC: Previous paragraphs focused on Fiber OC, this paragraph focuses on the applications of ANN in WOC systems including FSO, and VLC (which include few works). The most important issue in front of FSO system is the turbulence of the propagation media. The sensor-less adaptive optics could be used for compensating wavefront disturbance of FSO signal. Use of BP-ANN (Fig.10.a) for this task requires few online measurements compared with other model-based Fig. 8. a. an ANN, and b. computation process of a neuron. . approaches; this is good for real-time applications BIB021 . This was the only use of ANN in FSO, other works focused on VLC. By using White Light Emitting Diode (LED) in VLC could provide illumination and data transmission simultaneously BIB011 BIB015 . The most important issues of VLC include multipath-induced ISI BIB011 BIB012 , and fluorescent light interference (FLI) BIB015 , which cause high optical power penalty. The ANN could mitigate the ISI (when the transmission rate exceeds the system modulation bandwidth). Furthermore, it is shown that to in the presence of FLI and ISI, use of discrete wavelet transform and ANN based receiver has better performance compared with conventional methods of employing a high-pass filter and a finite impulse response equalizer BIB011 BIB015 . The same as single-input single-output (SISO) structure, ANN could also be used as equalizer in a multi-input multi-output (MIMO)-VLC system BIB013 . Filtering the blue component of the LED (at the cost of the power contribution of the yellowish wavelengths) is a popular method for data rate enhancement. However, with ANN equalizer, it is possible to obtain higher data rates from white light than blue component due to the obtained high SNR from maintaining the yellowish wavelengths BIB015 . Extreme learning machine ( Fig.10 .b) (ELM) is a feedforward ANN and can be used for classification, regression, clustering, sparse approximation, with a single layer or multiple hidden layers. The parameters of hidden nodes need not be tuned, and are randomly assigned and never updated, or can be inherited from their ancestors without being changed. Often, the output weights of hidden layers are learned in one step that essentially amounts to learning a linear model. Despite its good performance and speed, ELM in OC is only applied as a VLC MIMO detector in the presence of LED nonlinearity and cross-LED interference BIB034 . Most of ANN investigations in OC considered common NN layer types; however, ANN is not a fixed thing, the hyper parameters of ANN could be tuned manually BIB028 or greedy; in addition to these techniques, hyperparameters could be variable, for example number of neurons could be variable and an algorithm could be deployed to adaptively change it, this is ELM, and could be favorable for applications that no information is available about the data BIB022 .