Concurrent blind channel equalization with phase transmittance rbf neural networks
AUTOR(ES)
Loss, Diego Vier, Castro, Maria Cristina Felippetto De, Franco, Paulo Roberto Girardello, Castro, Fernando César Comparsi de
FONTE
Journal of the Brazilian Computer Society
DATA DE PUBLICAÇÃO
2007-03
RESUMO
This paper presents a new complex valued radial basis function (RBF) neural network (NN) with phase transmittance between the input nodes and output, which makes it suitable for channel equalization on quadrature digital modulation systems. The new Phase Transmittance RBFNN (PTRBFNN) differs from the classical complex valued RBFNN in that it does not strictly rely on the Euclidean distance between the input vector and the center vectors, thus enabling the transference of phase information from input to output. In the context of blind channel equalization, results have shown that the PTRBFNN not only solves the phase uncertainty of the classical complex valued RBFNN but also presents a faster convergence rate.comes the abstract of the paper.
Documentos Relacionados
- Frequency domain concurrent channel equalization for multicarrier systems
- Self-organized phase transitions in neural networks as a neural mechanism of information processing.
- Model Selection of RBF Networks Via Genetic Algorithms
- A DOE based approach for the design of RBF artificial neural networks applied to prediction of surface roughness in AISI 52100 hardened steel turning
- Synchronous neural networks of nonlinear threshold elements with hysteresis.