Machine Learning for Ultrawide Bandwidth Amplifier Configuration

Ionescu, Maria

doi:10.1109/icton.2019.8840453

Cited by 19 publications

(12 citation statements)

References 9 publications

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“…The last case, i.e. class C+L with 22 sub-bands ON, is the full load condition considered in [12]- [14], [16]. To summarize, depending on the load type (full or partial), we generate two families of data-sets:…”

Section: B Data-sets Generationmentioning

confidence: 99%

“…Lately, new approaches based on machine learning has been presented to determine the optimal pumps allocation for a target Raman gain profile [12]- [14]. In [12], a first proof-of-principle of the inverse model for a pure RA is shown on synthetic data, whilst in [13] the complete and extensive ML framework is reported and experimentally demonstrated.…”

Section: Introductionmentioning

confidence: 99%

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Introducing Load Aware Neural Networks for Accurate Predictions of Raman Amplifiers

Brusin

Moura

Curri

et al. 2020

J. Lightwave Technol.

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Section: B Data-sets Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Introducing Load Aware Neural Networks for Accurate Predictions of Raman Amplifiers

Brusin

Moura

Curri

et al. 2020

J. Lightwave Technol.

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“…Besides providing an increase in the SNR, a key additional advantage of Raman amplifiers is the ability to provide arbitrary gain profiles by adjusting the pump lasers' powers and frequencies. This additional flexibility has a wide-range of potential applications such as: complementing the gain of non-flat amplifiers like EDFAs [4], flattening of frequency combs, and maximizing the throughput in ultra-wideband systems by spectral shaping [1].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning

et al. 2021

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A machine learning framework predicting pump powers and noise figure profile for a target distributed Raman amplifier gain profile is experimentally demonstrated. We employ a single-layer neural network to learn the mapping from the gain profiles to the pump powers and noise figures. The obtained results show highly-accurate gain profile designs and noise figure predictions, with a maximum error on average of ∼0.3 dB. This framework provides the comprehensive characterization of the Raman amplifier and thus is a valuable tool for predicting the performance of the nextgeneration optical communication systems, expected to employ Raman amplification.

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“…Specifically, we also cite [24][25][26][27]. In [24], the authors utilize ML to predict the gain of a single EDFA and show that this method can provide improvements over an analytical model.…”

Section: Introductionmentioning

confidence: 99%

“…In [25] ML is used to predict the output of an EDFA cascade; in particular, wavelength assignment over a specific network considered in its entirety is able to be automated. Reference [26] investigates how ML can mitigate the effect of the EDFA gain ripple on QoT-E within a simulated network and [27] demonstrates how ML may be used to automatically configure the gain required by amplifiers after deployment. The main difference between this previous research and the present work is that we focus on the OSNR response to specific configurations in a particular OLS that is considered as an element of a completely disaggregated network.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning in an open optical line system controller

D’Amico

Curri

Straullu

et al. 2020

J. Opt. Commun. Netw.

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The reduction of system margin in open optical line systems (OLSs) requires the capability to predict the quality of transmission (QoT) within them. This quantity is given by the generalized signal-to-noise ratio (GSNR), including both the effects of amplified spontaneous emission (ASE) noise and nonlinear interference accumulation. Among these, estimating the ASE noise is the most challenging task due to the spectrally resolved working point of the erbium-doped fiber amplifiers (EDFAs), which depend on the spectral load, given the overall gain profile. An accurate GSNR estimation enables control of the power optimization and the possibility to automatically deploy lightpaths with a minimum margin in a reliable manner. We suppose an agnostic operation of the OLS, meaning that the EDFAs are operated as black boxes and rely only on telemetry data from the optical channel monitor at the end of the OLS. We acquire an experimental data set from an OLS made of 11 EDFAs and show that, without any knowledge of the system characteristics, an average extra margin of 2.28 dB is necessary to maintain a conservative threshold of QoT. Following this, we applied deep neural network machine-learning techniques, demonstrating a reduction in the needed margin average down to 0.15 dB.

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Machine Learning for Ultrawide Bandwidth Amplifier Configuration

Cited by 19 publications

References 9 publications

Introducing Load Aware Neural Networks for Accurate Predictions of Raman Amplifiers

Introducing Load Aware Neural Networks for Accurate Predictions of Raman Amplifiers

Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning

Using machine learning in an open optical line system controller

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