Hybrid Machine Learning EDFA Model

Zhu, Shengxiang; Gutterman, Craig; Montiel, Alan Diaz; Yu, Jiakai; Ruffini, Marco; Zussman, Gil; Kilper, Daniel C.

doi:10.1364/ofc.2020.t4b.4

Cited by 23 publications

(8 citation statements)

References 8 publications

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“…Correction for EDFA gain ripple has also been targeted in [40], [41], with [41] further using monitoring information to significantly reduce the margin of a network planning tool based upon the Gaussian noise (GN) model. Some recent works have moved beyond predicting ASE noise contributions in isolation; a hybrid approach is investigated in [42], demonstrating that the performance of common ML implementations may be enhanced by inclusion of an analytical model of EDFA gain. The improvement in ML predictions when considering a nonlinear transmission model has also been demonstrated in [43].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Lightpath QoT Computation With Machine Learning in Partially Disaggregated Optical Networks

D’Amico

Straullu

Borraccini

et al. 2021

IEEE Open J. Commun. Soc.

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Increasing traffic demands are causing network operators to adopt disaggregated and open networking solutions to better exploit optical transmission capacity, and consequently enable a softwaredefined networking (SDN) approach to control and management that encompasses the WDM data transport layer. In these frameworks, a quality of transmission estimator (QoT-E) that gives the generalized signalto-noise ratio (GSNR) is commonly used to compute the feasibility of transparent lightpaths (LP)s, taking into account the amplified spontaneous emission (ASE) noise and the nonlinear interference (NLI). In general, the ASE noise is the main contributor to the GSNR and is also the most challenging noise component to evaluate in a scenario with varying spectral loads, due to fluctuations in the optical amplifier responses. In this work, we propose a machine learning (ML) algorithm that is trained using different ASE-shaped spectral loads in order to predict the OSNR component of the GSNR; this methodology is subsequently used in combination with a QoT-E in the lightpath computation engine (L-PCE). We present an experiment on a point-to-point optical line system (OLS), including 9 commercial erbium-doped fiber amplifiers (EDFA)s used as black-boxes, each with variable gain and tilt values, and 8 fibers that are characterized by distinct physical parameters. Within this experiment, we receive the signal at the end of the OLS, measuring the bit-error-rate (BER) and the power spectrum, over 2520 different spectral loads. From this dataset, we extract the expected GSNRs and their linear and nonlinear components. Through joint application of a ML algorithm and the open-source GNPy library, we obtain a complete QoT-E, demonstrating that a reliable and accurate LP feasibility predictor may be implemented.INDEX TERMS Machine learning, optical communications, erbium doped fiber amplifier (EDFA), Raman scattering, quality of transmission (QoT).

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Section: Related Workmentioning

confidence: 99%

Enhancing Lightpath QoT Computation With Machine Learning in Partially Disaggregated Optical Networks

D’Amico

Straullu

Borraccini

et al. 2021

IEEE Open J. Commun. Soc.

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“…To further reduce the modeling error and training complexity for completely datadriven NN models, hybrid ML models that combine the analytical calculations and the ML-based schemes have been proposed. In [37], a hybrid ML gain model for an EDFA under AGC mode is discussed. Equation (3), which estimates the gain offset of an AGC EDFA, is used as prior knowledge and fed into the NN model.…”

Section: Ml-based Edfa Gain Modelsmentioning

confidence: 99%

Modeling EDFA Gain: Approaches and Challenges

Liu

et al. 2021

Photonics

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With the rapid development of virtual/augmented reality and cloud services, the capacity demand for optical communication systems is ever-increasing. To further increase system capacity, current researches focus on efficient and reliable system management, in which the transmission performance should be accurately estimated. The wavelength-dependent gain effects of erbium-doped fiber amplifiers (EDFAs) have a great impact on transmission performance, and therefore a precise EDFA gain model is required. In this paper, we firstly summarize the underlying principles and structures of EDFA, and introduce the gain performance and challenges in modeling. Then, we review the EDFA gain modeling methods. We categorize these researches into analytical modeling methods and machine learning (ML)-based modeling methods, and discuss their feasibilities and performances. In addition, we discuss the remaining problems for applying the models in a system and the possible directions for future investigations.

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“…However, the benefits in terms of accuracy improvement or margin reduction for QoT estimation were not presented. A most recent extension of EDFA gain spectrum modelling ( in [33] ) based on a hybrid (analytical and experimental dataset based neural network ML model) approach was presented in [34]. Some other recent ML-based works addressed the wavelength dependent gain spectra estimation [31], [35].…”

Section: A Related Work: Edfa Gain Ripplementioning

confidence: 99%

Modeling EDFA Gain Ripple and Filter Penalties With Machine Learning for Accurate QoT Estimation

Mahajan

Christodoulopoulos²,

Martínez³

et al. 2020

J. Lightwave Technol.

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For reliable and efficient network planning and operation, accurate estimation of Quality of Transmission (QoT) before establishing or reconfiguring the connection is necessary. In optical networks, a design margin is generally included in a QoT estimation tool (Qtool) to account for modeling and parameter inaccuracies, ensuring the acceptable performance. In this work, we use monitoring information from an operating network combined with supervised machine learning (ML) techniques to understand the network conditions. In particular, we model the penalties generated due to i.) Erbium Doped Fiber Amplifier (EDFA) gain ripple effect, and ii.) filter spectral shape uncertainties at Reconfigurable Optical Add and Drop Multiplexer (ROADM) nodes. Enhancing the Qtool with the proposed ML regression models yields estimates for new or reconfigured connections that account for these two effects, resulting in more accurate QoT estimation and a reduced design margin. We initially propose two supervised ML regression models, implemented with Support Vector Machine Regression (SVMR), to estimate the individual penalties of the two effects and then a combined model. On Deutsche Telekom (DT) network topology with 12 nodes and 40 bidirectional links, we achieve a design margin reduction of ~1dB for new connection requests.

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Hybrid Machine Learning EDFA Model

Abstract: A hybrid machine learning (HML) model combining a-priori and a-posteriori knowledge is implemented and tested, which is shown to reduce the prediction error and training complexity, compared to an analytical or neural network learning model.

Cited by 23 publications

References 8 publications

Enhancing Lightpath QoT Computation With Machine Learning in Partially Disaggregated Optical Networks

Enhancing Lightpath QoT Computation With Machine Learning in Partially Disaggregated Optical Networks

Modeling EDFA Gain: Approaches and Challenges

Modeling EDFA Gain Ripple and Filter Penalties With Machine Learning for Accurate QoT Estimation

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