Experimental Demonstration of Simultaneous Modulation Format/Symbol Rate Identification and Optical Performance Monitoring for Coherent Optical Systems

Guesmi, Latifa; Ragheb, Amr; Fathallah, Habib; Menif, Mourad

doi:10.1109/jlt.2017.2772851

Cited by 36 publications

(24 citation statements)

References 39 publications

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“…First, we applied sample normalization by division over sample's standard deviation. Then, we built the 2D IQH with size (80 × 80) and AAH with length 200 [46], [55]. Finally, the IQH H and IQH D are computed by applying the projection process to obtain features vectors with lengths 80 and 159, respectively, as discussed in Section II.…”

Section: Simulation Setupmentioning

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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Section: Simulation Setupmentioning

confidence: 99%

Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Saif

Ragheb

Alshawi

et al. 2021

J. Lightwave Technol.

Self Cite

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“…In paper [2], joint OPM and modulation format/Bit-Rate identification approaches based on CNN was proposed. Paper [3] illustrated the experimental demonstration of simultaneous MFI and symbol rate identification. Optical spectrum based OSNR monitoring approach was demonstrated in paper [4].…”

Section: Introductionmentioning

confidence: 99%

Modulation Format Identification Based on an Improved RBF Neural Network Trained With Asynchronous Amplitude Histogram

Zhou

Huang

et al. 2020

IEEE Access

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This paper proposed a method of modulation format identification using Radial Basis Function Artificial Neural Network (RBF-ANN) trained with Asynchronous Amplitude Histograms (AAHs). Compare with the traditional RBF-ANN, the proposed method is improved by applying Expectation Maximization (EM), which takes advantage of the statistical feature of AAHs, to select center vector for radial basis function. Assuming distribution of each bin in AAH as Gaussian mixture model (GMM), the mean values of the model can be exploited as the center vector which obtained using EM. This approach ensures that the center vector is unbiased and optimal. The center vector is implemented to RBF-ANN to identify different modulation formats. Numerical simulation results demonstrated that identification accuracy was about 99% for three commonly-used modulation formats within the OSNR between 40 ∼ 10dB. And the CD tolerance was 1000ps/nm. In comparison, former center vector selection approaches include K-means and random selection were applied. The result showed that the EM method improved the identification accuracy by 2% to 4% when OSNR = 10dB and CD = 100ps/nm. Owing to its excellent performance, this method can be employed in the next generation optical transport network for auto-adaption modulation format identification. INDEX TERMS Modulation format identification, artificial neural network, asynchronous amplitude histogram, expectation maximization.

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“…In recent years, researchers have utilized various technologies to achieve MFI such as signal cumulant and signal power distribution-based method [7,8], peak-to-average-power ratio of received data samples-based methods [9,10], Stokes space-based methods [11][12][13][14][15], and machine learning-based methods [16][17][18][19]. Various OSNR estimation techniques for coherent detection systems have been proposed recently including statistical moments [20], error vector magnitude [21], delay-line interferometer [22,23], Stokes parameters [24], Golay sequences [25], offset filtering, and optical power measurement [26], radio frequency (RF) spectrum [27], and amplitude noise correlation [28] based methods.…”

Section: Introductionmentioning

confidence: 99%

A Density Clustering Algorithm for Simultaneous Modulation Format Identification and OSNR Estimation

et al. 2020

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In this work, we propose a combined method to implement both modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation, a method based on density-based spatial clustering of applications with a noise (DBSCAN) algorithm. The proposed method can automatically extract the cluster number and density information of samples. The cluster number can be used for MFI and the density information combined with a fourth-order polynomial fitting can correctly estimate OSNR. We verify the feasibility of the method through simulation and conceptual proof experiments. The results show that the MFI can achieve 100% accuracy when the OSNR values are higher than the 7% forward error correction (FEC) thresholds for five commonly used modulation formats (MFs) like polarization division multiplexing (PDM)-QPSK, PDM-8PSK, PDM-16QAM, PDM-32QAM, and PDM-64QAM. Mean absolute OSNR estimation errors are not higher than 1 dB for different signals. There is no additional hardware required, so the proposed method has the ability to be integrated into existing optical performance monitoring systems without burden. Furthermore, the proposed method has the potential to be used in bit-error ratio (BER) calculation, linear, or nonlinear impairments monitoring. We believe that our multifunctional and simple method would be favorable to a future elastic optical network (EON).

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Experimental Demonstration of Simultaneous Modulation Format/Symbol Rate Identification and Optical Performance Monitoring for Coherent Optical Systems

Cited by 36 publications

References 39 publications

Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Optical Performance Monitoring in Mode Division Multiplexed Optical Networks

Modulation Format Identification Based on an Improved RBF Neural Network Trained With Asynchronous Amplitude Histogram

A Density Clustering Algorithm for Simultaneous Modulation Format Identification and OSNR Estimation

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