Design of a flattening filter using Fiber Bragg Gratings for EDFA gain equalization: an artificial neural network application
This paper presents a proposal for the non-uniform gain compensation of an Erbium-doped fiber optic amplifier (EDFA) in a Wavelength Division Multiplexed (WDM) system using Fiber Bragg Gratings (FBG). In this proposal, the multilayer perceptron feed-forward artificial neural network with back...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Editorial Neogranadina
2019-06-01
|
| Series: | Ciencia e Ingeniería Neogranadina |
| Subjects: | |
| Online Access: | https://revistasunimilitareduco.biteca.online/index.php/rcin/article/view/3818 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832539824698949632 |
|---|---|
| author | David Esteban Montoya Alba Jhonatan Mcniven Cagua Herrera Gustavo Adolfo Puerto Leguizam´ón |
| author_facet | David Esteban Montoya Alba Jhonatan Mcniven Cagua Herrera Gustavo Adolfo Puerto Leguizam´ón |
| author_sort | David Esteban Montoya Alba |
| collection | DOAJ |
| description |
This paper presents a proposal for the non-uniform gain compensation of an Erbium-doped fiber optic amplifier (EDFA) in a Wavelength 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.
|
| format | Article |
| id | doaj-art-7344684b09794d8b92905c855999c7f0 |
| institution | Kabale University |
| issn | 0124-8170 1909-7735 |
| language | English |
| publishDate | 2019-06-01 |
| publisher | Editorial Neogranadina |
| record_format | Article |
| series | Ciencia e Ingeniería Neogranadina |
| spelling | doaj-art-7344684b09794d8b92905c855999c7f02025-02-05T08:57:49ZengEditorial NeogranadinaCiencia e Ingeniería Neogranadina0124-81701909-77352019-06-01292Design of a flattening filter using Fiber Bragg Gratings for EDFA gain equalization: an artificial neural network applicationDavid Esteban Montoya Alba0https://orcid.org/0000-0003-4747-0002Jhonatan Mcniven Cagua Herrera1https://orcid.org/0000-0002-1683-9068Gustavo Adolfo Puerto Leguizam´ón2https://orcid.org/0000-0002-6420-9693Universidad Distrital Francisco José de CaldasUniversidad Distrital Francisco José de CaldasUniversidad Distrital Francisco José de Caldas This paper presents a proposal for the non-uniform gain compensation of an Erbium-doped fiber optic amplifier (EDFA) in a Wavelength 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. https://revistasunimilitareduco.biteca.online/index.php/rcin/article/view/3818Artificial neural networkEDFAflattening filterFiber Bragg GratingWavelength Division Multiplexing |
| spellingShingle | David Esteban Montoya Alba Jhonatan Mcniven Cagua Herrera Gustavo Adolfo Puerto Leguizam´ón Design of a flattening filter using Fiber Bragg Gratings for EDFA gain equalization: an artificial neural network application Ciencia e Ingeniería Neogranadina Artificial neural network EDFA flattening filter Fiber Bragg Grating Wavelength Division Multiplexing |
| title | Design of a flattening filter using Fiber Bragg Gratings for EDFA gain equalization: an artificial neural network application |
| title_full | Design of a flattening filter using Fiber Bragg Gratings for EDFA gain equalization: an artificial neural network application |
| title_fullStr | Design of a flattening filter using Fiber Bragg Gratings for EDFA gain equalization: an artificial neural network application |
| title_full_unstemmed | Design of a flattening filter using Fiber Bragg Gratings for EDFA gain equalization: an artificial neural network application |
| title_short | Design of a flattening filter using Fiber Bragg Gratings for EDFA gain equalization: an artificial neural network application |
| title_sort | design of a flattening filter using fiber bragg gratings for edfa gain equalization an artificial neural network application |
| topic | Artificial neural network EDFA flattening filter Fiber Bragg Grating Wavelength Division Multiplexing |
| url | https://revistasunimilitareduco.biteca.online/index.php/rcin/article/view/3818 |
| work_keys_str_mv | AT davidestebanmontoyaalba designofaflatteningfilterusingfiberbragggratingsforedfagainequalizationanartificialneuralnetworkapplication AT jhonatanmcnivencaguaherrera designofaflatteningfilterusingfiberbragggratingsforedfagainequalizationanartificialneuralnetworkapplication AT gustavoadolfopuertoleguizamon designofaflatteningfilterusingfiberbragggratingsforedfagainequalizationanartificialneuralnetworkapplication |