Detalhes bibliográficos
| Resumo: | We propose a new approach to fault detection and diagnosis in third-generation (3G) cellular networks using competitive neural algorithms. For density estimation purposes, a given neural model is trained with data vectors representing normal behavior of a CDMA2000 cellular system. After training, a normality profile is built from the sample distribution of the quantization errors of the training vectors. Then, we find empirical confidence intervals for testing hypotheses of normal/abnormal functioning of the cellular network. The trained network is also used to generate inference rules that identify the causes of the faults. We compare the performance of four neural algorithms and the results suggest that the proposed approaches outperform current methods. |
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| Autores principais: | Barreto, Guilherme de Alencar |
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| Outros Autores: | Mota, João César Moura; Souza, Luís Gustavo Mota; Frota, Rewbenio Araújo; Aguayo, Leonardo; Yamamoto, José Sindi; Macedo, Pedro Eduardo de Oliveira |
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| Ano: | 2023 |
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| País: | Brasil |
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| Tipo de documento: | outro |
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| Tipo de acesso: | acesso aberto |
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| Instituição associada: | IBICT |
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| Idioma: | inglês |
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| Origem: | Oasisbr |
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