Highly Accurate Measurement-Based Gain Model for Constant-Pump EDFA for non-Flat WDM Inputs

Meseguer, Alexis Carbo; Antona, Jean‐Christophe; Bononi, A.; Cho, Junho; Grubb, S.G.; Pecci, Pascal; Courtois, Olivier Le; Letellier, Vincent

doi:10.1364/ofc.2021.m5c.4

Cited by 12 publications

(10 citation statements)

References 4 publications

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“…In contrast to our previous work, in this work we show how to train a novel EDFA ML model on a system without direct access to the EDFA device itself and thus taking a further step towards practical systems. The proposed EDFA ML model is enhanced by data aided ML but guided by physical properties as proposed in Meseguer et al [35] and Saleh et al [48]. We show that such hybrid composition allows the EDFA model to be trained while the device is placed in the field, Fig.…”

Section: Introductionmentioning

confidence: 77%

“…by estimating the fiber type and fiber parameters of a deployed network [30]. If available, components can also be characterized in the lab before deployment by fitting a physical model or learning a model from data, as shown for erbium-doped fiber amplifiers (EDFAs) by [25,[31][32][33][34][35] and also in our previous work [36,37]. For operational networks, methods leveraging monitoring data have been proposed in [38,39].…”

Section: Introductionmentioning

confidence: 99%

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Spectral power profile optimization of a field-deployed wavelength-division multiplexing network enabled by remote EDFA modeling

Jones¹,

Bottrill²,

Taengnoi³

et al. 2023

J. Opt. Commun. Netw.

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We propose a technique for modeling erbium-doped fiber amplifiers (EDFAs) in optical fiber networks, where the amplifier unit is located at a distant node outside the laboratory. We collect data on an optical point-to-point link with the amplifier as the only amplification stage. Different amplifier operating points are modeled using probe signals and by adjusting the settings of the amplifier through a control network. The data are used to train a machine learning algorithm integrated within a physical EDFA model. The obtained mathematical model for the amplifier is used to model all amplifiers of a network and links with multiple amplification stages. To confirm the modeling accuracy, we thereafter predict and optimize launch power profiles of two selected links in the network of 439.4 km and 592.4 km lengths. Maximum/average channel optical signal-to-noise ratio prediction errors of 1.41/0.68 dB and 1.62/0.83 dB are achieved for the two multi-span systems, respectively, using the EDFA model trained on the single span system with margin-optimized launch power profiles. Up to 2.2 dB of margin improvements are obtained with respect to unoptimized transmission.

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Section: Introductionmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Spectral power profile optimization of a field-deployed wavelength-division multiplexing network enabled by remote EDFA modeling

Jones¹,

Bottrill²,

Taengnoi³

et al. 2023

J. Opt. Commun. Netw.

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“…An interesting question is how to possibly estimate the inversion x of a black-box commercial amplifier just from input/output measurements, and thus reconnect that estimate to the picture presented in this paper. Some recent black-box EDFA models point in that direction [20], [21], but the problem remains open. the fluorescence r τ , with τ the fluorescence time, iii) the forward and backward ASE flux Q F +B ASE , which causes ASE self-saturation, and is analytically included as [11]:…”

Section: Discussionmentioning

confidence: 99%

“…Ncj=1 Q j (G j (x 1 ) − 1), where N c (x M ) and the noise flux N j (x) in(21) are fixed, and λ > 0 is the Lagrange multiplier. We now set the derivative of the Lagrangian w.r.t.…”

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confidence: 99%

A State-Variable Approach to Submarine Links Capacity Optimization

Bononi

Serena

Antona³

2021

J. Lightwave Technol.

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We consider the capacity optimization of submarine links when including a realistic model of the gain-flattened constant-pump erbium doped fiber amplifiers (EDFA). While Perin et al. [1] numerically attacked this optimization for Constant-Gain (CG) amplified links, we extend the analysis also to more realistic submarine constant power-spectral-density (CPSD) links. As in [1], we concentrate on a single spatial mode of a spatial division multiplexed (SDM) link at low EDFA pump power Pp, and thus consider only the impairments of amplified spontaneous emission noise. Here we adopt a novel semi-analytical approach which consists of fixing the inversion x1 of the first EDFA (the state-variable of the link) and analytically finding capacity C(x1) by searching over the x1−feasible input wavelength division multiplexed (WDM) PSD distributions. Then the optimum inversion x1 that maximizes C(x1) is numerically obtained. This approach enables us to get both approximate (for CG links) and exact (for CPSD links) capacity-maximizing WDM input distributions, which vary inversely with the EDFA gain profile. For CG links the optimal WDM allocation is called the gain-shaped water-filling. Other practical allocations are analyzed, such as the signal to noise ratio equalizing allocation (CSNR), and the constant input power (CIP) allocation which uses a flat WDM distribution. We find that, for typical submarine span attenuations around 10dB and when the link works at the optimal inversion x1, CIP and CSNR achieve essentially the same capacity as the optimal allocation. At sufficiently large pump Pp ( 30 mW) the optimal inversion x1 is such that the EDFA gain at 1538nm equals the span attenuation, for EDFA emission and absorption as in [1]. When span attenuations increase to 20dB, then we start seeing an advantage of the optimal allocation. Another key finding is that optimized CG and CPSD links behave roughly the same, with a slightly superior capacity for CPSD.

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“…In contrast to our previous work [8] , where the EDFA is entirely modeled by neural networks (NNs), in this work a combination of a physical model and NNs is used. More specifically, the physical EDFA model presented in Meseguer et al [10] is modified, replacing both look up tables for the total power dependent gain profile and noise figure functions with NNs, ensuring that the resulting EDFA model is differentiable. Model training is performed in the following way, exemplified in Fig.…”

Section: Remote Edfa Modeling and Optimizationmentioning

confidence: 99%

Spectral Power Profile Optimization of Field-Deployed WDM Network by Remote Link Modeling

Jones¹,

Bottrill²,

Taengnoi³

et al. 2022

Preprint

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Highly Accurate Measurement-Based Gain Model for Constant-Pump EDFA for non-Flat WDM Inputs

Abstract: We develop a simple and accurate measurement-based model to predict the gain of wideband erbium-doped fiber amplifiers with a root mean square error of 0.05 dB, lower than state-of-the-art models based on machine learning techniques.

Cited by 12 publications

References 4 publications

Spectral power profile optimization of a field-deployed wavelength-division multiplexing network enabled by remote EDFA modeling

Spectral power profile optimization of a field-deployed wavelength-division multiplexing network enabled by remote EDFA modeling

A State-Variable Approach to Submarine Links Capacity Optimization

Spectral Power Profile Optimization of Field-Deployed WDM Network by Remote Link Modeling

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