Fiber Nonlinear Noise-to-Signal Ratio Monitoring Using Artificial Neural Networks

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

doi:10.1109/ecoc.2017.8345880

Cited by 25 publications

(21 citation statements)

References 5 publications

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“…By loading noise directly in the receiver, we are neglecting the nonlinear interactions of the noise and between the noise and the signal. This approach is commonly done is simulations [17] and should provide very similar results [20].…”

Section: Resultsmentioning

confidence: 99%

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Machine Learning Based Linear and Nonlinear Noise Estimation

Caballero¹,

Ives²,

Laperle

et al. 2018

J. Opt. Commun. Netw.

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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 optimizate the transmission characteristics, however this estimation is challenging. This paper addresses this problem by estimating the linear and nonlinear SNR from the received signal. This estimation is performed by obtaining features of two distinctive 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 fibre transmissions, we verified the accuracy of the proposed techniques. Employing both approaches simultaneously gave a measured performances of 0.04 and 0.20 dB of std error for the linear and nonlinear SNR, respectively.

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Section: Resultsmentioning

confidence: 99%

“…The limitation of the study of the covariance to the normal direction is motivated by the effect of phase noise from the transmitted and received laser over the tangential components, which can alter the measured covariance. A transformation to the ANC was proposed by [17]:…”

Section: A Amplitude Noise Covariancementioning

confidence: 99%

Machine Learning Based Linear and Nonlinear Noise Estimation

Caballero¹,

Ives²,

Laperle

et al. 2018

J. Opt. Commun. Netw.

Self Cite

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“…Recently machine learning techniques have emerged for robust estimation of the linear and nonlinear contributions to SNR based on neural network based regression [6], [7], [8], [9]. Herein we focus on neural networks, providing a basis for robust joint estimation of the linear and nonlinear noise contributions building on our previous work [9].…”

Section: Why Use Machine Learning?mentioning

confidence: 99%

Machine Learning Based Noise Estimation in Optical Fiber Communication Networks

Savory

Caballero

2018

2018 IEEE Photonics Society Summer Topical Meeting Series (SUM)

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“…In recent years, machine learning is a hot research area and has been applied to optical performance monitoring [17]- [19]. In [20], the method to monitor nonlinear signal-to-noise ratio (SNR nl ) used artificial neural networks (ANN) where the ANC proposed in [14] was used for training. After training with various systems, the ANN model for SNR nl monitoring achieved high accuracy.…”

Section: Introductionmentioning

confidence: 99%

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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Fiber Nonlinear Noise-to-Signal Ratio Monitoring Using Artificial Neural Networks

Cited by 25 publications

References 5 publications

Machine Learning Based Linear and Nonlinear Noise Estimation

Machine Learning Based Linear and Nonlinear Noise Estimation

Machine Learning Based Noise Estimation in Optical Fiber Communication Networks

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

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