An ultra-fast machine learning based method for accurate predictions of gain and amplified spontaneous emission (ASE) noise profiles of Raman amplifiers is introduced. It is an alternative to high-complexity and time-consuming standard approaches, which are based on the numerical solution of sets of nonlinear differential equations. Main relevance resides on its possible application in real-Time network controllers for future multi-band optical line systems where Raman amplification will be required to cope with capacities beyond the standard C-band. Here we consider as an example the C+L-band scenario with different input load conditions: full load and partial loads. For the case of full load it has been recently shown a neural network (NN) capable of highly accurate predictions. Real optical networks are not usually operated only in full load conditions: The load can dynamically vary over time and the behavior of the Raman amplifier depends on it. In this article we introduce a new NN model and we show its higher accuracy when the line system is not fully loaded: we define it as the load aware neural network. Applying this new approach we can predict both gain and ASE noise profiles in Raman amplifiers with high accuracy under any load conditions: we demonstrate almost 100% of maximum prediction errors to be lower than 0.5 dB.
Introducing Load Aware Neural Networks for Accurate Predictions of Raman Amplifiers / Rosa Brusin, A. M.; De Moura, U. C.; Curri, V.; Zibar, D.; Carena, A.. - In: JOURNAL OF LIGHTWAVE TECHNOLOGY. - ISSN 0733-8724. - STAMPA. - 38:23(2020), pp. 6481-6491.
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo
|Titolo:||Introducing Load Aware Neural Networks for Accurate Predictions of Raman Amplifiers|
|Data di pubblicazione:||2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/JLT.2020.3014810|
|Appare nelle tipologie:||1.1 Articolo in rivista|
File in questo prodotto:
|09160884.pdf||2a Post-print versione editoriale / Version of Record||Non Pubblico - Accesso privato/ristretto||Administrator Richiedi una copia|
|JLT___ML4RA__Load_Aware_NN_PrePrint.pdf||Post-print||2a Post-print versione editoriale / Version of Record||PUBBLICO - Tutti i diritti riservati||Visibile a tuttiVisualizza/Apri|