Concurrent Blind Channel Equalization with Phase Transmittance RBF Neural Networks

Diego Vier LossMaria C. F. De CastroPaulo Roberto Girardello FrancoFernando C. C. De Castro

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.

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