Design of a flattening filter using Fiber Bragg Gratings for EDFA gain equalization: an artificial neural network application

  • David Montoya Alba
  • Jhonatan Cagua Herrera
  • Gustavo Puerto Leguizamon
Palabras clave: Artificial Neural Network, EDFA, Flattening Filter

Resumen

This paper presents a proposal for the compensation of the non-uniform gain of an Erbium-doped fiber optic amplifier (EDFA) in Wavelength Division Multiplexed (WDM) system using Fiber Bragg Gratings (FBG). In this proposal, the multilayer perceptron feed-forward artificial neural network with backward propagation was trained under the secant method (one-step secant) and was selected according to mean square error measurement. The proposal optimizes the parameters of the FBG such as center frequency, rejection level and length in order to determine a filtering response based on a reduced number of FBGs used to flatten the non-linear response of the amplifier gain. The proposal was evaluated in an amplified WDM system of eight optical carriers located from 195 THz to 196.4 THz.

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Citas

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Cómo citar
Montoya Alba, D., Cagua Herrera, J., & Puerto Leguizamon, G. (2019). Design of a flattening filter using Fiber Bragg Gratings for EDFA gain equalization: an artificial neural network application. Ciencia E Ingeniería Neogranadina, 29(2). https://doi.org/10.18359/rcin.3818
Publicado
2019-06-20
Sección
Artículos