Novel OSNR Measurement Techniques Based on Optical Spectrum Analysis and Their Application to Coherent-Detection Systems

This paper proposes a signal-to-noise ratio (SNR) estimator based on recurrent neural network (RNN) in optical fiber communication links. The proposed estimator jointly estimates the linear and nonlinear components of the SNR. The input features of the proposed estimator are carefully designed based on a combination of the lower quartile and entropy extracted from the received signal. The proposed input features do not require knowledge of the transmitted symbols. In the proposed SNR estimator, three different RNN models are investigated, namely simple RNN, gated recurrent units, and long shortterm memory. The overall computational complexity of the three models of the proposed estimator, including the feature extraction and RNN structures, are analyzed. Numerical results show that the three models of the proposed estimator provide a trade-off between the complexity of the RNN structure and estimation accuracy. Furthermore, the proposed estimator achieves a better SNR estimation accuracy and reduces the overall computational complexity compared to the literature.

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“…The OSNR can be measured using OSA-based techniques [13]- [16] or calculated approximately by [17,] OSNR(dB) ≈ 58 + P − (10 log 10 (S)…”

Section: System Modelmentioning

confidence: 99%

“…4. This figure shows the overall computational complexity comparison between the estimators at N = 2 16 . It is shown that the proposed RNNbased estimators achieve the lowest overall computational complexity in terms of real additions.…”

Section: Comparison With Literature Estimatorsmentioning

confidence: 99%

Learned Signal-to-Noise Ratio Estimation in Optical Fiber Communication Links

Al-Nahhal

Dobre

et al. 2022

IEEE Photonics J.

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“…On the other side, NDA methods have the benefit of in-service estimation without reducing the SE. These include: (i) statistical moments based approaches [13], [14], which experience degradation of performance when considered for multilevel constellations [15], (ii) empirical cumulative distribution function based approaches [15], which only suit multilevel modulation schemes, and (iii) correlationbased OSNR estimation methods [16], which fail to distinguish the noise level in the presence of fiber nonlinearity [17]. The next important aspect is CD monitoring.…”

Section: Introductionmentioning

confidence: 99%

Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Saif

Ragheb

Alshawi

et al. 2021

J. Lightwave Technol.

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This paper considers, for the first time, optical performance monitoring (OPM) in few mode fiber (FMF)-based optical networks. One dimensional (1D) features vector, extracted by projecting a two-dimensional (2D) asynchronous in-phase quadrature histogram (IQH), and the 2D IQH are proposed to achieve OPM in FMF-based network. Three machine learning algorithms are employed for OPM and their performances are compared. These include support vector machine, random forest algorithm, and convolutional neural network. Extensive simulations are conducted to monitor optical to signal ratio (OSNR), chromatic dispersion (CD), and mode coupling (MC) for dual polarization-quadrature phase shift keying (DP-QPSK) at 10, 12, 16, 20, and 28 Gbaud transmission speeds. Besides, M-ary quadrature amplitude modulation (M = 8 and 16) is considered. Also, the OPM accuracy is investigated under different FMF channel conditions including phase noise and polarization mode dispersion. Simulation results show that the proposed 1D projection features vector provides better OPM results than those of the widely used asynchronous amplitude histogram (AAH) features. Furthermore, it has been found that the 2D IQH features outperform the 1D projection features but require larger number of features samples. Additionally, the effect of fiber nonlinearity on the OPM accuracy is investigated. Finally, OPM using the 2D IQH features has been verified experimentally for 10 Gbaud DP-QPSK signal. The obtained results show a good agreement between both simulation and experimental findings.

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“…The first type can be typically enabled at the destination node by digital coherent receivers, such as amplitude histograms [5], statistical moments [6], error vector magnitude (EVM) [7], Stokes parameters [8], amplitude noise correlation [9] and data-aided [10]- [12] based methods. While the second type can be typically enabled at the intermediate nodes by using some additional devices, such as polarization nulling [13], optical delay interferometer [14], offset filtering and optical power measurement [15], reference optical spectrum [16]- [19] and asynchronous delay-tap plots (ADTPs) [20] based methods. In general, an OSNR monitor should be cost-effective, reliable, robust, and distributed across the transmission link for obtaining link-level information.…”

Section: Introductionmentioning

confidence: 99%

A Novel Cost-Effective and Distributed in-Band OSNR Monitoring Method Using Gaussian Process Regression

Wei

Zheng

et al. 2019

IEEE Photonics J.

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We propose and experimentally demonstrate a novel cost-effective and distributed in-band optical signal-to-noise ratio (OSNR) monitoring method using a widely tunable optical bandpass filter and optical power measurements, which employs the Gaussian process regression and is proved to be insensitive to fiber nonlinearity, chromatic dispersion, and optical amplifier type. This method is verified in a 9-channel 10 Gbaud NRZ-QPSK coherent optical transmission system with 50-GHz channel intervals. When there are no add-drop filters or wavelength selective switches in the transmission link, the maximum OSNR deviation, the root-mean-squared error (RMSE), and the mean absolute error (MAE) are less than 0.21 dB, 0.041 dB, and 0.021 dB, respectively, in the OSNR range of 1-29 dB. Instead, when there exist the above optical filtering elements in the transmission link, the maximum OSNR deviation, the RSME, and the MAE are less than 0.24 dB, 0.041 dB, and 0.021 dB, respectively, in the OSNR range of 4-31 dB. Besides, the OSNR monitor based on the novel method can eliminate the necessity to know the transmission information and is more convenient to use because no calibration is required.

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Novel OSNR Measurement Techniques Based on Optical Spectrum Analysis and Their Application to Coherent-Detection Systems

Cited by 16 publications

References 13 publications

Learned Signal-to-Noise Ratio Estimation in Optical Fiber Communication Links

Learned Signal-to-Noise Ratio Estimation in Optical Fiber Communication Links

Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

A Novel Cost-Effective and Distributed in-Band OSNR Monitoring Method Using Gaussian Process Regression

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