Nonlinear Signal-to-Noise Ratio Estimation in Coherent Optical Fiber Transmission Systems Using Artificial Neural Networks

Kashi, Aazar Saadaat; Zhuge, Qunbi; Cartledge, J.C.; Etemad, Sadegh; Borowiec, A.; Charlton, D.; Laperle, Charles; O’Sullivan, Maurice

doi:10.1109/jlt.2018.2873949

Cited by 43 publications

(24 citation statements)

References 15 publications

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“…In previous work [20]- [22], two other features, amplitude noise correlation (ANC), ANC(m) = E( A(k) A(k+m) ) and phase noise correlation (PNC), PNC(m) = E( P(k) P(k+m) ) are defined and used to estimate nonlinear noise power. Where A(k) and P(k) are the amplitude noise and phase noise of the k th symbol, respectively.…”

Section: Theoretical Principle a Evc: A New Feature To Charactermentioning

confidence: 99%

“…These statistics, together with ANC, are used in ANN training to estimate SNR nl . In [22], another statistical characteristic phase noise correlation (PNC) was added to train the ANN model in [20], which made the method more accurate. However, it is found that the estimation accuracy depends on the symbol patterns.…”

Section: Introductionmentioning

confidence: 99%

“…If the test symbol patterns are not included in the trained stage, the estimation error will be greater. For methods in [20]- [22], the characteristics used in these methods reflect amplitude or phase information separately. It is known that nonlinear distortion is manifested both in amplitude and phase of the signal, so the error vector containing both amplitude and phase information can be utilized.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Nonlinear Noise Power Estimation Method Based on Error Vector Correlation Function Using Artificial Neural Networks For Coherent Optical Fiber Transmission Systems

Yang

Guo

et al. 2020

IEEE Access

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In this paper, we propose a promising nonlinear noise power estimation method based on correlation functions using artificial neural networks (ANN), which is robust against both amplifier spontaneous emission noise and the symbol patterns. Error vector correlation (EVC), together with the amplitude noise correlation (ANC) and the phase noise correlation (PNC), is used as the input of ANN in the proposed method. 378 cases of 224 Gb/s polarization-multiplexed 16-quadrature amplitude modulated (PM-16-QAM) signal transmitted over a wide range of conditions by varying launch power, OSNR and transmission distance are used to train and test the ANN. With the launch power varying from 0 to 8 dBm and the transmission distance as long as 2400 km, the results of tested samples demonstrate that the maximum absolute deviation (MAD), mean absolute error (MAE) and root mean square error (RMSE) of the estimated nonlinear noise power are 0.65 dB, 0.20 dB and 0.27 dB, respectively. In order to verify the independence of symbol patterns, 150 new cases with 30 pseudo-random binary sequence (PRBS) seeds are used to test the trained ANN. The results show that the MAE, MAD and RMSE of estimated nonlinear noise power are 0.86 dB, 0.25 dB and 0.33 dB respectively, meaning that the trained ANN is valid even if the test samples are not covered by the trained process. The results in this work verified that the ANN with EVC, ANC and PNC as input can make the trained model feasible to accurately estimate the nonlinear noise power in high speed optical coherent communication systems.

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Section: Theoretical Principle a Evc: A New Feature To Charactermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Nonlinear Noise Power Estimation Method Based on Error Vector Correlation Function Using Artificial Neural Networks For Coherent Optical Fiber Transmission Systems

Yang

Guo

et al. 2020

IEEE Access

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“…With the recent advances in machine learning, many machine learning techniques have been employed and have become popular research tools in the field of optical fiber communication. For example, k-means clustering and Gaussian mixture models (GMM) have been used for constellation clustering [18,19]; support vector machines (SVM) have been used for nonlinear classification [20,21]; and different kinds of NNs have been used in areas such as modulation format identification (MFI) [22,23], optical performance monitoring (OPM) [24,25] and nonlinear equalization in optical fiber transmission systems [9][10][11][12][13][14][15][16][17]. The nonlinear activation function in each hidden layer allows NNs to approximate complex nonlinear functions quite well, making them suitable for dealing with the nonlinear distortions in short-reach DD systems.…”

Section: Introductionmentioning

confidence: 99%

Computational complexity comparison of feedforward/radial basis function/recurrent neural network-based equalizer for a 50-Gb/s PAM4 direct-detection optical link

Sun

et al. 2019

Opt. Express

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The computational complexity and system bit-error-rate (BER) performance of four types of neural-network-based nonlinear equalizers are analyzed for a 50-Gb/s pulse amplitude modulation (PAM)-4 direct-detection (DD) optical link. The four types are feedforward neural networks (F-NN), radial basis function neural networks (RBF-NN), auto-regressive recurrent neural networks (AR-RNN) and layer-recurrent neural networks (L-RNN). Numerical results show that, for a fixed BER threshold, the AR-RNN-based equalizers have the lowest computational complexity. Amongst all the nonlinear NN-based equalizers with the same number of inputs and hidden neurons, F-NN-based equalizers have the lowest computational complexity while the AR-RNN-based equalizers exhibit the best BER performance. Compared with F-NN or RNN, RBF-NN tends to require more hidden neurons with the increase of the number of inputs, making it not suitable for long fiber transmission distance. We also demonstrate that only a few tens of multiplications per symbol are needed for NN-based equalizers to guarantee a good BER performance. This relatively low computational complexity signifies that various NN-based equalizers can be potentially implemented in real time. More broadly, this paper provides guidelines for selecting a suitable NN-based equalizer based on BER and computational complexity requirements.

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“…But, these electrical methods are dependent on complex measurement devices, and difficult to be applied in the intermediate transmission links. In addition, many digital signal processing (DSP) based in-band OSNR measurement methods, such as neural network algorithms [16][17][18], training sequence assisted technique [19], were also studied. Certainly, an additional photoelectric noise introduced by the coherent receiver has an influence on the accuracy of OSNR measurement.…”

mentioning

confidence: 99%

In-Band OSNR Measurement Method for All-Optical Regenerators in Optical Domain

Wan

Wen

et al. 2019

Applied Sciences

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We propose an in-band measurement method of optical signal-to-noise ratio (OSNR) output from an all-optical regeneration system with a nonlinear power transfer function (PTF) according to the fact that there are different average gains of signal and noise. For the all-optical quadrature phase-shift keying (QPSK) regenerator as an example, the output OSNR is derived from the input OSNR and the total gain of the degraded QPSK signal. Our simulation shows that the OSNR results obtained by this method are in agreement with those calculated from the error vector magnitude (EVM) formula. The method presented here has good applicability for different data rates but is also useful for analyzing the OSNR degradation of other nonlinear devices in optical communication links. IntroductionWith the exponential growth of data information, optical communication networks are becoming more and more intelligent and elastic [1]. Optical performance monitoring (OPM), especially in optical signal-to-noise ratio (OSNR), will play an important role in optical link diagnosis, flexible bandwidth allocation, and fast network service deployment [2]. In high-speed long-haul optical transmission systems, amplified spontaneous emission (ASE) noise from erbium-doped fiber amplifier (EDFA) is one of the main factors resulting in the degradation of the transmitted signals. Different from fiber dispersion and nonlinearity, it cannot be eliminated by some compensation methods. In this case, all-optical regeneration technology has attracted much attention [3], especially to high-order modulation signal regeneration, such as multi-level amplitude regeneration based on nonlinear interference structure [4,5], multi-stage phase regeneration based on phase-sensitive four-wave mixing (FWM) [6,7], and several regeneration schemes of quadrature amplitude modulation (QAM) signals [8,9].Just like in-line EDFAs, all-optical regenerators are usually integrated into the intermediate nodes of optical links. However, most of the previous studies paid more attention to the regeneration performance in the electrical domain, instead of the optical domain. In other words, the OSNR improvement for all-optical regenerators is seldom considered in experiments due to a lack of more accurate OSNR measurement methods in the optical domain. As is well-known, the traditional OSNR measurement method used in WDM systems is based on the spectral interpolation technique by optical spectrum analysis [10]. Unfortunately, the use of optical filters or all-optical regenerators can give rise to the out-band distortion of the optical spectrum distribution, and then the validation of the interpolation measurement. Secondly, the coherent receivers are extensively used in high-order modulation systems such as those of quadrature phase-shift keying (QPSK) or QAM signals, so the performance of all-optical regenerators is often evaluated in the electrical domain as well. In fact, there are

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Nonlinear Signal-to-Noise Ratio Estimation in Coherent Optical Fiber Transmission Systems Using Artificial Neural Networks

Cited by 43 publications

References 15 publications

A Novel Nonlinear Noise Power Estimation Method Based on Error Vector Correlation Function Using Artificial Neural Networks For Coherent Optical Fiber Transmission Systems

A Novel Nonlinear Noise Power Estimation Method Based on Error Vector Correlation Function Using Artificial Neural Networks For Coherent Optical Fiber Transmission Systems

Computational complexity comparison of feedforward/radial basis function/recurrent neural network-based equalizer for a 50-Gb/s PAM4 direct-detection optical link

In-Band OSNR Measurement Method for All-Optical Regenerators in Optical Domain

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