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Concurrent blind channel equalization with phase transmittance RBF neural networks

Abstract

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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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Loss, D.V., De Castro, M.C.F., Franco, P.R.G. et al. Concurrent blind channel equalization with phase transmittance RBF neural networks. J Braz Comp Soc 13, 18–25 (2007). https://doi.org/10.1007/BF03192398

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  • DOI: https://doi.org/10.1007/BF03192398

Keywords

  • Neural Network
  • Equalizer
  • Phase
  • Concurrent
  • Blind