An Accurate Nonlinear Noise Insensitive OSNR Monitor

Li, Dou; Yamauchi, Takeshi; Su, Xiaofei; Tao, Zhenning; Oda, Shunri; Aoki, Yuriko; Hoshida, Takeshi; Rasmussen, Jens C.

doi:10.1364/ofc.2016.w3a.5

Cited by 15 publications

(12 citation statements)

References 4 publications

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“…In this paper, the application of machine learning techniques for optical performance monitoring are investigated. Optical performance monitoring is vital to ensure robust and reliable networks [15], [24], [25]. The modulation format and the optical signal to noise ratio (OSNR) are key parameters for assessing the performance of optical transmission links.…”

Section: Clustering K-meansmentioning

confidence: 99%

Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals

Thrane

Wass

Piels

et al. 2017

J. Lightwave Technol.

147

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Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, while the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical signal to noise ratio (OSNR) estimation and modulation format classification, respectively. The proposed methods accurately evaluate optical signals employing up to 64 quadrature amplitude modulation (QAM), at 32 Gbaud, using only directly-detected data.

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Section: Clustering K-meansmentioning

confidence: 99%

Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals

Thrane

Wass

Piels

et al. 2017

J. Lightwave Technol.

147

View full text Add to dashboard Cite

show abstract

“…The first type utilizes linear or nonlinear optical schemes, such as the polarization-nulling technique [6], an optical delay interferometer (ODI) [7], a non-linear loop mirror [8], a fiber parametric device [9], [10] or an optical detector designed for two-photon absorption [11]. The second type of methods relies on digital coherent optical receivers [12]- [14]. Each of these methods has some pros and cons.…”

Section: Introductionmentioning

confidence: 99%

“…But some of them are not effective to dual-polarization signals and the others have a relatively small tolerance to the transmission distortions, such as CD and PMD [6]- [10]. For the second type of methods, they have large tolerance to CD and PMD because advanced DSP algorithms can be applied to mitigate the impact of these distortions in advance [12]- [14]. However, with these methods OSNR can only be monitored within the full-fledged digital coherent optical receivers, which are not cost-effective for the network-wide deployment.…”

Section: Introductionmentioning

confidence: 99%

A Robust Reference Optical Spectrum Based in-Band OSNR Monitoring Method Suitable for Flexible Optical Networks

Zhong

Cui

et al. 2020

IEEE Photonics J.

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The in-band OSNR monitoring methods based on the noise-free reference optical spectrum (ROS) have attracted much attention in the field of optical performance monitoring because they are cost-effective and insensitive to chromatic dispersion and polarization mode dispersion. In this paper, we propose a new ROS based in-band OSNR monitoring method that is robust to the distortions due to non-ideal modulation, the variations of the in-line EDFA gain and the number of filtering elements traversed by the signal. Moreover, this method supports using the standard ROS retrieved by the existing machinelearning technique based optical spectrum identification technique, and thus can remove the need to measure the ROS near the transmitter, which is hard to perform, especially in flexible optical networks. These merits make the method more robust and convenient to use in practice. Extensive numerical simulations are presented to demonstrate the merits of the proposed method with respect to the existing ROS based methods.

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“…The capability to accurately estimate signal nonlinear distortion has potential application to nonlinear compensation as well as system capacity prediction. However, it is challenging to distinguish between nonlinear distortion and optical amplifier spontaneous emission (ASE) noise when estimating nonlinear noise power [10]. There are mainly two ways to separate NL from ASE noise.…”

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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An Accurate Nonlinear Noise Insensitive OSNR Monitor

Cited by 15 publications

References 4 publications

Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals

Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals

A Robust Reference Optical Spectrum Based in-Band OSNR Monitoring Method Suitable for Flexible Optical 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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