Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning

Wang, Danshi; Zhang, Min; Cai, Zhongle; Cui, Yue; Li, Ze; Han, Huanhuan; Fu, Meixia; Luo, Bin

doi:10.1016/j.optcom.2016.02.029

Cited by 66 publications

(19 citation statements)

References 25 publications

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“…Although machine learning based equalization techniques have been extensively studied in wireless systems [14,15], only lately they have been considered for application in fibre transmission systems. A number of techniques, such as, k-nearest neighbors algorithm [16], affinity propagation clustering [17], statistical sequence equalizers [18], expectation maximization algorithms with Gaussian mixture models [19] and support vector machines [20][21][22], have been proposed combining the nonlinear equalization (NLE) functionality with optimum symbol classification. This means that they can adapt their decision boundaries to the residual nonlinear distortion of the received signal instead of performing hard decision.…”

Section: Introductionmentioning

confidence: 99%

Equalization performance and complexity analysis of dynamic deep neural networks in long haul transmission systems

Sidelnikov¹,

Redyuk²,

Sygletos³

2018

Opt. Express

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We investigate the application of dynamic deep neural networks for nonlinear equalization in long haul transmission systems. Through extensive numerical analysis we identify their optimum dimensions and calculate their computational complexity as a function of system length. Performing comparison with traditional back-propagation based nonlinear compensation of 2 steps-per-span and 2 samples-per-symbol, we demonstrate equivalent mitigation performance at significantly lower computational cost.

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Section: Introductionmentioning

confidence: 99%

Equalization performance and complexity analysis of dynamic deep neural networks in long haul transmission systems

Sidelnikov¹,

Redyuk²,

Sygletos³

2018

Opt. Express

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“…During the training process it is necessary to adapt certain parameters of the optimization problem to the characteristics of the input data. The aim is to avoid over-or underfitted systems, which leads to a significantly reduced classification accuracy [12]. The adaptation and verification is implemented using the two optimization algorithms Grid-Search and Cross Validation [12,18].…”

Section: Svm-based Detectionmentioning

confidence: 99%

“…The aim is to avoid over-or underfitted systems, which leads to a significantly reduced classification accuracy [12]. The adaptation and verification is implemented using the two optimization algorithms Grid-Search and Cross Validation [12,18]. In the optimization process, 70% of the training data is used for training and 30% for validation.…”

Section: Svm-based Detectionmentioning

confidence: 99%

“…The advantage of extended signal detection by SVM is already emphasized in references [3,[12][13][14]. In order to investigate exclusively the influence of nonlinearities such as NLPN or SPM, the influence of dispersion has been neglected deliberately in the past [3,12]. The absence of dispersion means that the interaction between dispersion and nonlinearities is not investigated.…”

Section: Introductionmentioning

confidence: 99%

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Mitigation of Nonlinear Impairments by Using Support Vector Machine and Nonlinear Volterra Equalizer

et al. 2019

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A support vector machine (SVM) based detection is applied to different equalization schemes for a data center interconnect link using coherent 64 GBd 64-QAM over 100 km standard single mode fiber (SSMF). Without any prior knowledge or heuristic assumptions, the SVM is able to learn and capture the transmission characteristics from only a short training data set. We show that, with the use of suitable kernel functions, the SVM can create nonlinear decision thresholds and reduce the errors caused by nonlinear phase noise (NLPN), laser phase noise, I/Q imbalances and so forth. In order to apply the SVM to 64-QAM we introduce a binary coding SVM, which provides a binary multiclass classification with reduced complexity. We investigate the performance of this SVM and show how it can improve the bit-error rate (BER) of the entire system. After 100 km the fiber-induced nonlinear penalty is reduced by 2 dB at a BER of 3.7 × 10 − 3 . Furthermore, we apply a nonlinear Volterra equalizer (NLVE), which is based on the nonlinear Volterra theory, as another method for mitigating nonlinear effects. The combination of SVM and NLVE reduces the large computational complexity of the NLVE and allows more accurate compensation of nonlinear transmission impairments.

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“…Moreover, all proposed nonlinearity compensators present high complexity [3][4][5][6][7] being impractical for real-time communications. The aforementioned random noises of the network can be partially tackled by low-complex digital machine learning algorithms that perform nonlinear equalization (NLE), such as unsupervised and supervised algorithms: machine learning clustering (MLC) with K-means and Gaussian mixture [8][9][10], and classification machines [11], e.g. artificial neural networks (ANN) [12][13][14] and convolutional neural network-based deep learning [15,16].…”

mentioning

confidence: 99%

Blind Nonlinearity Equalization by Machine-Learning-Based Clustering for Single- and Multichannel Coherent Optical OFDM

Giacoumidis

Matin

Wei

et al. 2018

J. Lightwave Technol.

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Abstract-Fiber-induced intra-and inter-channel nonlinearities are experimentally tackled using blind nonlinear equalization (NLE) by unsupervised machine learning based clustering (MLC) in ~46 Gb/s single-channel and ~20 Gb/s (middle-channel) multi-channel coherent multi-carrier signals (OFDM-based). To that end we introduce, for the first time, Hierarchical and FuzzyLogic C-means (FLC) based clustering in optical communications. It is shown that among the two proposed MLC algorithms, FLC reveals the highest performance at optimum launched optical powers (LOPs), while at very high LOPs Hierarchical can compensate more effectively nonlinearities only for low-level modulation formats. When employing BPSK and QPSK, FLC outperforms K-means, Fast-Newton support vector machines, supervised artificial neural networks and NLE with deterministic Volterra analysis. In particular, for the middle channel of a QPSK WDM coherent optical OFDM system at optimum -5 dBm of LOP and 3200 km of transmission, FLC outperforms Volterra-NLE by 2.5 dB in Q-factor. However, for a 16-QAM single-channel system at 2000 km, the performance benefit of FLC over IVSTF reduces to ~0.4 dB at a LOP of 2 dBm (optimum). Even when using novel sophisticated clustering designs in 16 clusters, no more than additional ~0.3 dB Q-factor enhancement is observed. Finally, in contrast to the deterministic Volterra-NLE, MLC algorithms can partially tackle the stochastic parametric noise amplification.

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Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning

Cited by 66 publications

References 25 publications

Equalization performance and complexity analysis of dynamic deep neural networks in long haul transmission systems

Equalization performance and complexity analysis of dynamic deep neural networks in long haul transmission systems

Mitigation of Nonlinear Impairments by Using Support Vector Machine and Nonlinear Volterra Equalizer

Blind Nonlinearity Equalization by Machine-Learning-Based Clustering for Single- and Multichannel Coherent Optical OFDM

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