A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> C. Deep Neuronal Network <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> BIB001 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> C. Deep Neuronal Network <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> BIB002 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> C. Deep Neuronal Network <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> BIB003 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> C. Deep Neuronal Network <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> BIB004 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> C. Deep Neuronal Network <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> BIB005 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> C. Deep Neuronal Network <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> BIB006 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> C. Deep Neuronal Network <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> BIB007 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> C. Deep Neuronal Network <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> BIB008 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> C. Deep Neuronal Network <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> BIB009 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> C. Deep Neuronal Network <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> BIB010
Definition: Deep learning is a branch of ML that uses different types of NNs, and is becoming especially exciting now, because there are more amounts of data. Deep learning includes different structures such as DNN, deep belief networks, recurrent neural networks and convolutional neural networks (Fig.11. a, b, c, d ). Each of these techniques has success in some specific applications (they are data dependent), and can't compare them unless being used on the same data set. DNN is an extension of ANN that could derive more complex relationships between the input and output by use of multiple additional hidden layers. Fiber OC: Exactly the same as ANN, the main application of DNN is equalization, but because DNN could find more complex relationships, it could be used for more complex channel/system models. In OC, the Shannon capacity (the fundamental barrier of the maximum achievable rate) is mainly attributed to linear and nonlinear distortions  , fiber Kerr effect  , and ASE noise. Earlier impractical approaches compensated only deterministic nonlinearities, however, DNN tackles the interplay of deterministic and stochastic nonlinearity manifestation BIB003 . Furthermore, DNN could compensate nonlinear inter-carrier crosstalk effects even in the presence of frequency stochastic variations. Non-orthogonal waveform signals could improve spectral efficiency, but they cause interference. To solve this issue, could use the spectrally efficient frequency division multiplexing, but it is a complex approach. DNN could be well used to compensate linear and nonlinear interference with low complexity  . Another approach for this task is using DNN based on unrolling the splitstep Fourier method (SSFM) BIB004 . This technique (DNN based Digital Back Propagation (DBP)) significantly reduces the complexity compared to conventional DBP, and compared to standard "black-box" DNN. DNN moves through the layers calculates the probability of each output. For example, in symbol detection, DNN calculates the probability that a specific symbol is received. Due to improvements in DNN compared with ANN, this idea could be extended from SISO (in ANN) to MIMO (in DNN) systems, and DNN is applied in scenarios such as Mode Division Multiplexing (MDM) BIB006 , and Spatial Division Multiplexing (SDM) BIB007 . For example BIB006 used DNN to implement a somehow inverse channel matrix and use the result for detection, or BIB007 used Elman NN (ENN) (Fig.12.a) for traffic prediction in an EON. ENN is a typical ANN, with additional layer called context layer, which is connected to the hidden layer with a weight of one, and are used to save previous values of the hidden layer. The second application of DNN is joint monitoring and identification. Again, the same as ANN, the amplitude histograms (AH)s, obtained after the constant modulus algorithm (CMA) equalization, could be used as feature for MFI BIB001 , or joint MFI and OSNR monitoring by DNN BIB002 . DNN is simple and low cost because it is non-data-aided and avoids any additional hardware on top of the standard digital coherent receiver. In addition MFI using DNN has higher accuracy compared with ANN. The third application is end to end DNN, which has emerged in OC recently. Mostly, DNN is used as a tool at the receiver side; however, recently, some works investigated the effect of using it at both sides of transreceiver. Actually, end to end DNN enables joint optimization of OC system transceiver parameters. All elements of the transceiver and the fiber channel could be modeled, and by DNN the transceiver configuration could be adjusted by minimizing a loss function. This technique could be used for auto encoding  BIB008  or constellation shaping BIB005 85] , and power adjustment [86] in OC systems. The method is used as is without any further analytic derivations necessary since the ML optimization method is agnostic to the embedded channel model. Long short term memory (LSTM) RNN (Fig.13.a) is one type of RNN, which is composed of a cell, an input gate, an output gate and a forget gate. The cell saves values over arbitrary time intervals and the gates regulate data flow into and out of the cell. OSNR monitoring is important for achieving a reliable and reconfigurable network with high QoS. The use of LSTM-RNN is useful for identifying the OSNR relationship of timevaried data with high accuracy and short response time BIB009 . The forth and interesting application of DNN is modelling system parts, e.g., optical amplifier BIB010 , and optical modulator  . For doing this task DNN learns the relationship between features and targets (e.g. Raman gain profile and pump powers and wavelengths). It is proven that modeling devices using DNN has high accuracy, low latency, and low Fig. 10. a. Back propagation NN, b. ELM NN. . complexity. The most important parameter in modeling, is the DNN layer type, which is commonly feedforward layer ( Fig.12.b) , in which data flows in forward direction. However, it could not model the channel memory effect, and for this task, recurrent layer should be used.