Estimação Precisa da Qualidade de Transmissão com Redes Neurais Profundas para Sistemas Ópticos Densos

João Vitor A Garcês, Igor Monteiro Moraes, Diogo M. F. Mattos

DOI: 10.14209/sbrt.2023.1570923779

Evento: XLI Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2023)

Keywords: Deep Neural Network GSNR prediction Optical Networks OSNR

Abstract

The advent of new Internet technologies and ap- plications has increased the need for high-speed and reliable data transport. DWDM optical networks use Transmission Qua- lity (QoT) metrics, such as Generalized Signal-to-Noise Ratio (GSNR), for channel provisioning. This parameter requires computationally expensive analytical prediction. Thus, this article proposes a model of deep neural networks to estimate the Gene- ralized Signal-to-Noise Ratio accurately and quickly. The model is validated through simulations, which in turn are validated with data from a real network in operation. The results show an absolute percentage error of less than 0.54% between the estimation obtained by the proposed model and the simulated value.

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