Related papers: On the Accuracy of the GN-Model and on Analytical …
The GN model of non-linear fiber propagation has been shown to overestimate the variance of non-linearity due to the signal Gaussianity approximation, leading to maximum reach predictions for realistic optical systems which may be…
The GN model of non-linear fiber propagation has been shown to overestimate the variance of non-linearity due to the signal Gaussianity approximation, leading to maximum reach predictions for typical optical systems which may be pessimistic…
Efficient evaluation of nonlinearity in modern coherent optical telecommunication networks is necessary for design, optimization and management. The original GN model formula can not be used in real time applications due to long processing…
The enhanced Gaussian noise (EGN) model is widely used for estimating the nonlinear interference (NLI) power accumulated in coherent fiber-optic transmission systems. Given a fixed fiber link, under the assumption that transmitted symbols…
An unprecedented comparison of closed-form incoherent GN (InGN) models is presented with heterogeneous spans and partially loaded links in elastic optical networks. Results reveal that with accumulated dispersion correction and modulation…
In this paper, we present an accurate and numerically efficient method to implement the GN and EGN nonlinearity prediction methods when the power evolution along the fiber is in an arbitrary form. This approach will provide us with a…
Recently, a perturbative model of non-linear fiber propagation in uncompensated optical transmission systems has been proposed, called GN-model [1]. Here, an extended and more detailed version of the GN-model derivation [1] is reported,…
An analytical model to estimate nonlinear performance in ultra-wideband optical transmission networks is presented. The model accurately accounts for inter-channel stimulated Raman scattering, variably loaded fibre spans and is validated…
Fiber nonlinear interference (NLI) modeling and monitoring are the key building blocks to support elastic optical networks (EONs). In the past, they were normally developed and investigated separately. Moreover, the accuracy of the…
We derived an approximate non-linear interference (NLI) closed-form model (CFM), capable of handling a very broad range of optical WDM system scenarios. We tested the CFM over 8500 randomized C-band WDM systems, of which 6250 were…
Knowing only two high-order statistical moments of modulation symbols, often represented by the fourth moment called "kurtosis", the overestimation of nonlinear interference (NLI) in a Gaussian noise (GN) model due to Gaussian signaling…
In this paper, we propose to use the discretized version of the so-called Enhanced Gaussian Noise (EGN) model to estimate the non-linearity effects of fiber on the performance of optical coherent and uncompensated transmission (CUT)…
We derived a fully-closed-form GN-model formula and tested its accuracy of over 7,000 highly randomized Cband system scenarios. By further applying a correction that leverages the large system test-set, we were able to substantially improve…
We present a novel closed-form model for nonlinear interference (NLI) estimation in low-dispersion O-band transmission systems. The formulation incorporates the four-wave mixing (FWM) efficiency term as well as the coherent contributions of…
Ultrafast accurate physical layer models are essential for designing, optimizing and managing ultrawideband optical transmission systems. We present a closed-form GN/EGN model based on a recent analytical breakthrough, improving…
The enhanced Gaussian noise (EGN) model, which accounts for inter-channel stimulated Raman scattering (ISRS), has been extensively utilized for evaluating nonlinear interference (NLI) within the C+L band. Compared to closed-form expressions…
In this paper we report on the details of the derivation of approximate closed-form results related to the coherence effect in non-linear noise accumulation, in the context of the GN-model of fiber non-linearity. The coherence effect is…
Graph Neural Networks (GNNs) have shown success in learning from graph structured data containing node/edge feature information, with application to social networks, recommendation, fraud detection and knowledge graph reasoning. In this…
Despite Graph Neural Networks (GNNs) have achieved prominent success in many graph-based learning problem, such as credit risk assessment in financial networks and fake news detection in social networks. However, the trained GNNs still make…
Presently with technology node scaling, an accurate prediction model at early design stages can significantly reduce the design cycle. Especially during logic synthesis, predicting cell congestion due to improper logic combination can…