| Texto completo | |
| Autor(es): |
Fantinato, Denis G.
;
Neves, Aline
;
Silva, Daniel G.
;
Attux, Romis
;
Ueda, N
;
Watanabe, S
;
Matsui, T
;
Chien, JT
;
Larsen, J
Número total de Autores: 9
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2017 IEEE 27TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING; v. N/A, p. 6-pg., 2017-01-01. |
| Resumo | |
In communication systems, the study of elements and structures defined over Galois fields are generally limited to data coding. However, in this work, a novel perspective that combines data coding and channel equalization is considered to compose a simplified communication system over the field. Besides the coding advantages, this framework is able to restore distortions or malfunctioning processes, and can be potentially applied in network coding models. Interestingly, the operation of the equalizer is possible from a blind standpoint through the exploration of the redundant information introduced by the encoder. More specifically, we define a blind equalization criterion based on the matching of probability mass functions (PMFs) via the Kullback-Leibler divergence. Simulations involving the main aspects of the equalizer and the criterion are performed, including the use of a genetic algorithm to aid the search for the solution, with promising results. (AU) | |
| Processo FAPESP: | 17/11488-5 - Análise Multivariada da Estrutura Temporal de Dados para Separação Cega de Fontes no Contexto de Misturas Não Lineares e de Múltiplos Conjuntos de Dados |
| Beneficiário: | Denis Gustavo Fantinato |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |
| Processo FAPESP: | 13/14185-2 - Novas Metodologias de Aprendizado Baseado na Teoria da Informação para Equalização Adaptativa |
| Beneficiário: | Denis Gustavo Fantinato |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |