arXiv subjects
Publications and source records attributed to Pierluigi Poggiolini.
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 it and obtain EGN-model accuracy with real-time-compatible computation effort.
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 time needed for numerical 2-D integration, particularly when facing with full C band or C+L band. There are closed form approximations of the GN model which has been mathematically derived based on some assumptions which fail in low dispersion regime. In this work, we derive a GN closed form formula without any restrictive assumption capable of handling arbitrary WDM combs and arbitrary link structures in closed form which can be used with a very good accuracy with respect to original GN formula for networks employing both high and low dispersion fibers.
The GN-model of fiber non-linearity has had quite substantial success in modern optical telecommunications networks as a design and management tool. A version of it, capable of handling arbitrary WDM combs and link structures in closed form, was proposed in 2014. Here we upgrade that formula, to make it capable of handling frequency-dependent dispersion, frequency-dependent loss and frequency-dependent gain/loss due to Stimulated Raman Scattering (SRS) among channels. This way, more challenging and complex network scenarios, like the ones that are being deployed right now, can be dealt with in real-time, to the great advantage of management, control and optimization of such networks.
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, providing deeper insight into the model. Some straightforward generalizations of the model are also proposed.