Resumen

MONTOYA ALBA, David Esteban; CAGUA HERRERA, Jhonatan Mcniven  y  PUERTO LEGUIZAMON, Gustavo Adolfo. Design of a Flattening Filter Using Fiber Bragg Gratings for EDFA Gain Equalization: An Artificial Neural Network Application. Cienc. Ing. Neogranad. [online]. 2019, vol.29, n.2, pp.25-36.  Epub 20-Jun-2019. ISSN 0124-8170.  https://doi.org/10.18359/rcin.3818.

This paper presents a proposal for the non-uniform gain compensation of an Erbiumdoped fiber optic amplifier (EDFA) in a Wave-length Division Multiplexed (WDM) system using Fiber Bragg Gratings (FBG). In this proposal, the multilayer perceptron feed-forward artificial neural network with backpropagation was trained under the secant method (one-step secant) and was selected according to mean square error measurement. The proposal optimizes FBG parameters such as center frequency, rejection level and length in order to determine a filtering response based on a reduced number of FBGs that will be used to flatten the non-linear response of the amplifier gain and avoid the per-carrier treatment of a standard flattening filter. While an artificial neural network with a 7-10-6 structure demonstrated the feasibility of equalizing the gain of an EDFA using as few as three FBGs, a 25-18-12 structure improved the results when the configuration consisted of an FBG array of six resonances that provided similar results to that featured by the standard gain-flattening filter. The proposal was evaluated in an amplified WDM system of eight optical carriers located between 195-196.4 THz.

Palabras clave : Artificial neural network; EDFA; flattening filter; Fiber Bragg Grating; Wavelength Division Multiplexing.

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