Demonstration of in-service wavelength division multiplexing optical-signal-to-noise ratio performance monitoring and operating guidelines for coherent data channels with different modulation formats and various baud rates

Chitgarha, Mohammad Reza; Khaleghi, Salman; Daab, Wajih; Almaiman, Ahmed; Ziyadi, Morteza; Mohajerin-Ariaei, Amirhossein; Rogawski, D.; Tur, Moshe; Touch, Joseph D.; Vusirikala, Vijay; Zhao, Wendy; Willner, Alan E.

doi:10.1364/ol.39.001605

Cited by 27 publications

(6 citation statements)

References 13 publications

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“…The sequential minimal optimization (SMO) [26], [27] algorithm can be adapted for the solution of this convex quadratic programming problem depicted in Eq. (7). Then the optimal mapping direction w can be calculated as:…”

Section: Support Vector Ordinal Regressionmentioning

confidence: 99%

OSNR Monitoring Using Support Vector Ordinal Regression for Digital Coherent Receivers

Hao

Yan

et al. 2019

IEEE Photonics J.

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An OSNR monitoring method assisted by support vector ordinal regression (SVOR) is proposed for digital coherent receivers. According to Godard's error of equalized signals obtained after polarization demultiplexing, the optical signal-to-noise ratio (OSNR) value could be effectively estimated by using SVOR. The effectiveness of the proposed OSNR monitoring method is verified via numerical simulations with 28-Gbaud PDM-QPSK, PDM-8PSK, PDM-8QAM, PDM-16QAM and PDM-32QAM signals. The simulation results show that the 100% accuracies for the five modulation formats can be obtained over a wide OSNR range. The feasibility is further demonstrated by proof-of-concept experiments in 28-Gbaud PDM-QPSK, PDM-8PSK, PDM-8QAM and PDM-16QAM systems. The errors of averaged values of estimated OSNR for all the signals are less than or equal to 0.2 dB.

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Section: Support Vector Ordinal Regressionmentioning

confidence: 99%

OSNR Monitoring Using Support Vector Ordinal Regression for Digital Coherent Receivers

Hao

Yan

et al. 2019

IEEE Photonics J.

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“…Reference [94] shows its robustness and accuracy to the transmitter parameters drift in a various reconfigurable networking conditions is investigated for 25-Gbaud PM-QPSK and PM-16-QAM signals. Reference [95] demonstrates its format transparency and baud rate tunability and gives an operating guidelines for a desired level of monitoring accuracy for coherent data channels including the shape of the filter spectrum, the bandwidth of the filter, DLI delay, and DLI phase-detuning. In [96], the monitor accuracy is experimentally examined when the initially measured calibration parameters of the DLI-based monitor for a 25-Gbaud PM-QPSK signal are kept fixed without updating in the presence of various systems changes by modifying the transmitter and link parameters.…”

Section: Opm At the Intermediate Network Nodes Using Low-cost Structuresmentioning

confidence: 99%

Optical Performance Monitoring: A Review of Current and Future Technologies

Dong

Khan

Shu

et al. 2016

J. Lightwave Technol.

279

100

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Optical Performance Monitoring (OPM) is the estimation and acquisition of different physical parameters of transmitted signals and various components of an optical network. OPM functionalities are indispensable in ensuring robust network operation and plays a key role in enabling flexibility and improve overall network efficiency. We review the development of various OPM techniques for direct-detection systems and digital coherent systems and discuss future OPM challenges in flexible and elastic optical networks.

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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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Demonstration of in-service wavelength division multiplexing optical-signal-to-noise ratio performance monitoring and operating guidelines for coherent data channels with different modulation formats and various baud rates

Cited by 27 publications

References 13 publications

OSNR Monitoring Using Support Vector Ordinal Regression for Digital Coherent Receivers

OSNR Monitoring Using Support Vector Ordinal Regression for Digital Coherent Receivers

Optical Performance Monitoring: A Review of Current and Future Technologies

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

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