OSNR prediction using machine learning-based EDFA models

You, Yuren; Jiang, Zhiping; Janz, C.

doi:10.1049/cp.2019.1044

Cited by 5 publications

(3 citation statements)

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“…The prediction was related to the WDM measurements as the output of the model was the power difference between fully loaded (WDM) and partially loaded (arbitrary) channel power. [16] considers optical signal-to-noise ratio (OSNR) prediction using EDFA models that use two different models to predict gain profile and noise figure, separately, with an additional OSA for data collection.…”

Section: Ml-based Edfa Modelsmentioning

confidence: 99%

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Open EDFA gain spectrum dataset and its applications in data-driven EDFA gain modeling

Wang

Kilper²,

Chen

2023

J. Opt. Commun. Netw.

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Optical networks satisfy high bandwidth and low latency requirements for telecommunication networks and data center interconnection. To improve network resource utilization, machine learning (ML) is used to accurately model optical amplifiers such as erbium-doped fiber amplifiers (EDFAs), which impact end-to-end system performance such as quality of transmission. However, a comprehensive measurement dataset is required for ML to accurately predict an EDFA’s wavelength-dependent gain. We present an open dataset consisting of 202,752 gain spectrum measurements collected from 16 commercial-grade reconfigurable optical add–drop multiplexer (ROADM) booster and pre-amplifier EDFAs under varying gain settings and diverse channel-loading configurations over 2,785 hours in total, with a total dataset size of 3.1 GB. With this EDFA dataset, we implemented component-level deep-neural-network-based EDFA models and use transfer learning (TL) to transfer the EDFA model among 16 ROADM EDFAs, which achieve less than 0.18/0.24 dB mean absolute error for booster/pre-amplifier gain prediction using only 0.5% of the full target training set. We also showed that TL reduces the EDFA data collection requirements on a new gain setting or a different type of EDFA on the same ROADM.

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Section: Ml-based Edfa Modelsmentioning

confidence: 99%

“…Total # of meas. Fixed Baseline 95 × 1 (WDM), 48 × 2 (lower/odd), 47 × 2 (upper/even), 1 × 7 (single), 2 × 7 (double) 5 (WDM), 2 (single/double), 2 (lower/upper/odd/even) 20 (WDM), 5 (single/double), 5 (lower/upper/odd/even) 280Fixed Goalpost[2,4,8,16, 32] × 3 (balanced),[9,18] × 6 (imbalanced) 2, . .…”

mentioning

confidence: 99%

Open EDFA gain spectrum dataset and its applications in data-driven EDFA gain modeling

Wang

Kilper²,

Chen

2023

J. Opt. Commun. Netw.

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“…In this context, data-driven techniques, especially neural networks (NNs), have attracted increasing attention in recent years 24 , 26 , 27 , 36 – 38 However, a bottleneck in implementing data-driven models is the requirement for large training data sets to achieve high levels of accuracy. For example, with about 12,000 pieces of measured data, the RMSE of the gain model for an EDFA can be reduced to 0.1 dB 26 .…”

Section: Introductionmentioning

confidence: 99%

Digital twin modeling and controlling of optical power evolution enabling autonomous-driving optical networks: a Bayesian approach

Liu,

Zhang,

Chen

et al. 2024

Adv. Photon.

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Optical networks are evolving toward ultrawide bandwidth and autonomous operation. In this scenario, it is crucial to accurately model and control optical power evolutions (OPEs) through optical amplifiers (OAs), as they directly affect the signal-to-noise ratio and fiber nonlinearities. However, a fundamental contradiction arises between the complex physical phenomena in optical transmission and the required precision in network control. Traditional theoretical methods underperform due to ideal assumptions, while data-driven approaches entail exorbitant costs associated with acquiring massive amounts of data to achieve the desired level of accuracy. In this work, we propose a Bayesian inference framework (BIF) to construct the digital twin of OAs and control OPE in a data-efficient manner. Only the informative data are collected to balance the exploration and exploitation of the data space, thus enabling efficient autonomous-driving optical networks (ADONs). Simulations and experiments demonstrate that the BIF can reduce the data size for modeling erbium-doped fiber amplifiers by 80% and Raman amplifiers by 60%. Within 30 iterations, the optimal controlling performance can be achieved to realize target signal/gain profiles in links with different types of OAs. The results show that the BIF paves the way to accurately model and control OPE for future ADONs.

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A Meta-Learning-Assisted Training Framework for Physical Layer Modeling in Optical Networks

Liu

Lun

Liu

et al. 2022

J. Lightwave Technol.

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OSNR prediction using machine learning-based EDFA models

Cited by 5 publications

References 0 publications

Open EDFA gain spectrum dataset and its applications in data-driven EDFA gain modeling

Open EDFA gain spectrum dataset and its applications in data-driven EDFA gain modeling

Digital twin modeling and controlling of optical power evolution enabling autonomous-driving optical networks: a Bayesian approach

A Meta-Learning-Assisted Training Framework for Physical Layer Modeling in Optical Networks

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