A Closed-form Expression for the ISRS GN Model Supporting Distributed Raman Amplification

Buglia, Henrique; Jarmolovicius, M.; Vasylchenkova, Anastasiia; Sillekens, Eric; Galdino, Lídia; Bayvel, Polina; Killey, Robert I.

doi:10.1364/ofc.2023.w2a.29

Cited by 1 publication

(2 citation statements)

References 8 publications

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“…The data set for training is generated by simulations based on the GNPy, 50 which is a commonly-utilized Python tool for calculating the fiber nonlinearity based on Gaussian noise model 22 , 51 . The simulation setup is shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…In multiband ADONs, OPE is mainly influenced by fiber propagation and amplification process. Since the fiber propagation process, including attenuation and ISRS, can be accurately calculated, 20 – 22 the main challenge in modeling and controlling OPE lies in optical amplifiers (OAs). However, modeling and controlling an erbium-doped fiber amplifier (EDFA) 23 in current C-band systems are already difficult tasks 24 – 27 due to the complex wavelength-dependent gain characteristics of EDFAs in dynamic link conditions 28 , 29 .…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

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.

View full text Add to dashboard Cite

show abstract

A Closed-form Expression for the ISRS GN Model Supporting Distributed Raman Amplification

Abstract: A closed-form model for the nonlinear interference in distributed Raman amplified links is presented, the formula accounts for both forward and backward pumping. The model accurately estimates the received SNR over a 10 THz bandwidth.

Cited by 1 publication

References 8 publications

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

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

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