Design considerations for low-margin elastic optical networks in the nonlinear regime [Invited]

Savory, Seb J.; Vincent, R.; Ives, D. J. G.

doi:10.1364/jocn.11.000c76

Cited by 14 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To indicate the possible gains from low-margin links we reproduce the analysis from [54], where it is assumed that the capacity per polarization is given by the Shannon capacity for the additive white Gaussian noise channel [55]…”

Section: B Possible Gains From Low-margin Operationmentioning

confidence: 99%

Flexible Technologies to Increase Optical Network Capacity

et al. 2022

Self Cite

View full text Add to dashboard Cite

Increased global traffic puts tough requirements not just on fibre communications links, but on the entire network. This manifests itself in multiple ways including how to optimise wavelength routing around the network; how to maximise the benefits arising from fine-control DSP with increasingly accurate real-time monitoring; and how to best deploy Multiband or multiple fibre connectivity. This paper will summarise research into all these areas to present a full picture of how future optical networks will play their role in supporting the continuing traffic demands of broadband, 5G and associated applications.

show abstract

Section: B Possible Gains From Low-margin Operationmentioning

confidence: 99%

Flexible Technologies to Increase Optical Network Capacity

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…They also explored the effects of variation in the operating conditions of span EDFAs (monitored by tapping the signal at each span EDFA) to minimize the OSNR penalty due to gain ripple. The authors in [30], [31] proposed a preemphasis launch power technique to reduce the EDFA gain ripple effects. This technique is well suited for fully loaded links and requires precise control at the transmitter.…”

Section: A Related Work: Edfa Gain Ripplementioning

confidence: 99%

“…This technique is well suited for fully loaded links and requires precise control at the transmitter. The work in [31] use approximate analytical models to calculate noise at full channel loading conditions. However [31] verified once more that EDFA gain ripple affects the quality of signal, hence SNR.…”

Section: A Related Work: Edfa Gain Ripplementioning

confidence: 99%

“…The work in [31] use approximate analytical models to calculate noise at full channel loading conditions. However [31] verified once more that EDFA gain ripple affects the quality of signal, hence SNR. A gradient descent-based launch power optimization strategy was used to improved SNR in this work.…”

Section: A Related Work: Edfa Gain Ripplementioning

confidence: 99%

“…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]. In [36], [37] neural network models were also proposed to recommend wavelength assignment with 99% of precision value that considers EDFA gain ripple effects.…”

Section: A Related Work: Edfa Gain Ripplementioning

confidence: 99%

See 2 more Smart Citations

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

Mahajan

Christodoulopoulos²,

Martínez³

et al. 2020

J. Lightwave Technol.

View full text Add to dashboard Cite

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.

show abstract

Reducing blocking probability and QoT violation in dynamic elastic optical networks via load-aware margin selection

Habibi

Beyranvand²

2022

Computer Networks

View full text Add to dashboard Cite

Design considerations for low-margin elastic optical networks in the nonlinear regime [Invited]

Cited by 14 publications

References 18 publications

Flexible Technologies to Increase Optical Network Capacity

Flexible Technologies to Increase Optical Network Capacity

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

Reducing blocking probability and QoT violation in dynamic elastic optical networks via load-aware margin selection

Contact Info

Product

Resources

About