Proposta de equalizador cego baseado em algoritmos genéticos
This paper proposes a convex optimization scheme based on linear programming and genetic algorithms for the blind equalizers applied to digital communications systems. It arose from the growing need for improvements in communication systems in order to transmit as much information as possible in...
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| Formato: | Dissertação |
| Idioma: | por |
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Brasil
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| Endereço do item: | https://repositorio.ufrn.br/jspui/handle/123456789/21975 |
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| Resumo: | This paper proposes a convex optimization scheme based on linear programming and
genetic algorithms for the blind equalizers applied to digital communications systems.
It arose from the growing need for improvements in communication systems in order to
transmit as much information as possible in a physical environment reliably. The proposed scheme, ELC-GA (Blind Linear Equalizer Linear based on Genetic Algorithms), is characterized by performing blind adaptive channel equalization in fixed units of data, using a genetic algorithm as adaptive algorithm, whose objective function is a globally convergent constrained linear function. However, due to the random characteristics
of the signal modeled with intersymbol interference and additive white Gaussian noise, the used linear function now represents a stochastic linear programming. Accordingly, the use of genetic algorithms is particularly suitable for being able to get optimal solutions covering a considerable portion of the search space, which corresponds to the various stochastic scenarios. This work also describes the implementation details of the proposed scheme and the performed computational simulations. In the performance analysis, the ELC- GA results are compared to the results of one of the traditional blind equalization techniques, CMA,
used as reference in this analysis. The results are shown and discussed under the appropriate
metric analysis. The conclusions of the study indicate the GA - ELC as a promising alternative to blind
equalization due to its equalization performance, which reaches global convergence in a considerably smaller range of symbols than the technique used as reference. |
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