Novel OSNR Measurement Techniques for Coherent-Detection Systems

Gariepy, Daniel; Searcy, Steven; He, Gang; Tibuleac, Sorin

doi:10.1364/ofc.2018.th1a.1

“…Optical performance monitoring is a well established research area that attempts to monitor optical transmission systems with a view to assessing the quality of a transmission link [3], albeit herein we focus on the estimation of linear and nonlinear noise contributions. Classical approaches include fitting a three parameter model [4] to separate the nonlinear and linear noise contributions or estimating the ASE noise component from an optical spectrum analyzer [5].…”

Section: Why Use Machine Learning?mentioning

confidence: 99%

Machine Learning Based Noise Estimation in Optical Fiber Communication Networks

Savory

¹

,

Caballero

²

2018

2018 IEEE Photonics Society Summer Topical Meeting Series (SUM)

2

0

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“…In frequency domain, ref. [9] obtain a noise partitioning based a comparison of the received spectra w.r.t. a reference spectrum, using a commercial OSA.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly [10], proposed a pseudo-analytical method to estimate the noise-to-signal spectrum based on an incoherent GN model. The method of [9] operates predominantly on the spectral transitions at high and low frequency edges and, as such, is vulnerable to the action of optical filtering present in the optical line. Furthermore, [15] combined neural networks with spectral resolved techniques to monitor nonlinearities and optimum launch power.…”

Section: Introductionmentioning

confidence: 99%

Perturbation-Based Frequency Domain Linear and Nonlinear Noise Estimation

Vaquero-Caballero

¹

,

Ives

²

,

Savory

³

2022

J. Lightwave Technol.

3

0

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In this paper, a new method for the separation of noise categories based on Four-Wave Mixing is presented.The theoretical analysis is grounded in the Gaussian Noise model and verified by split step simulations. The noise categories react differently to the introduced perturbations, by performing a set of perturbations the behaviour of the different categories can be separated by means of a leastsquare fitting. Given ASE is independent of the induced perturbations, it is possible to separate noise contributions. The analysis includes constant and variable power perturbations.The estimation of the noise categories is discussed from two points of view: NSR evolution post-DSP processing, and over the power spectral density in a notched region. The NSR estimation can only be performed at reception, whereas the power spectral density approach can be performed along the optical link if a high resolution Optical Spectrum Analyzer is available.Additionally, we perform a simple experimental verification considering of two WaveLogic 3 transceivers for the NSR, successfully estimating the nonlinear noise contributions.

show abstract

“…Similarly [10], proposed a pseudo-analytical method to estimate the noise-to-signal spectrum based on an incoherent GN model. The method of [9] operates predominantly on the spectral transitions at high and low frequency edges and, as such, is vulnerable to the action of optical filtering present in the optical line.…”

Section: Introductionmentioning

confidence: 99%

Perturbation-based Frequency Domain Linear and Nonlinear Noise Estimation

Vaquero-Caballero,

Ives,

Savory

2021

Preprint

0

View full text Add to dashboard Cite

In this paper, a new method for the separation of noise categories based on Four-Wave Mixing is presented.The theoretical analysis is grounded in the Gaussian Noise model and verified by split step simulations. The noise categories react differently to the introduced perturbations, by performing a set of perturbations the behaviour of the different categories can be separated by means of a leastsquare fitting. Given ASE is independent of the induced perturbations, it is possible to separate noise contributions. The analysis includes constant and variable power perturbations.The estimation of the noise categories is discussed from two points of view: NSR evolution post-DSP processing, and over the power spectral density in a notched region. The NSR estimation can only be performed at reception, whereas the power spectral density approach can be performed along the optical link if a high resolution Optical Spectrum Analyzer is available.Additionally, we perform a simple experimental verification considering of two WaveLogic 3 transceivers for the NSR, successfully estimating the noise contributions.

show abstract

Novel OSNR Measurement Techniques for Coherent-Detection Systems

Cited by 6 publications

References 1 publication

Machine Learning Based Noise Estimation in Optical Fiber Communication Networks

Machine Learning Based Noise Estimation in Optical Fiber Communication Networks

Perturbation-Based Frequency Domain Linear and Nonlinear Noise Estimation

Perturbation-based Frequency Domain Linear and Nonlinear Noise Estimation

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