| Texto completo | |
| Autor(es): |
Chaves, F. E. C.
;
Rosa, E. S.
;
Maciel, J. A. S.
;
Sutili, T.
;
Figueiredo, R. C.
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2024 SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, SBFOTON IOPC 2024; v. N/A, p. 3-pg., 2024-01-01. |
| Resumo | |
In order to verify and validate the use of machine learning techniques for chromatic dispersion compensation in optical transmissions, we elaborated an end-to-end recurrent neural network aiming at replacing the conventional digital signal processing (DSP) blocks employed at the transmitter and the receiver. Moreover, in order to establish a valid comparison between the conventional DSP and the integrated neural networks, we evaluate the computational complexity of each method for the same simulation scenario. Despite presenting a good performance for the compensation of chromatic dispersion, the end-to-end technique presented a significantly greater computational complexity, limiting its potential use in practical applications. (AU) | |
| Processo FAPESP: | 21/06569-1 - Tecnologias estratégicas para internet de alta velocidade |
| Beneficiário: | Evandro Conforti |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |