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Optimal blind equalization of Gaussian channels

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New Trends in Neural Computation (IWANN 1993)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 686))

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Abstract

In this paper we show that a recurrent version of a Radial Basis Functions network can compute optimal symbol-by-symbol decissions for equalizing Gaussian channels in digital communication systems, when the (linear or not) channel response and the noise variance are known. In order to do this, the recurrent RBF (RRBF) computes several statistics about the possible states of the channel, which can be used to estimate the channel response during operation. Taking advantage of this fact, a novel technique for learning the channel parameters in a non-supervised way is theoretically derived, resulting a simple and fast algorithm that can be used for tracking in time variant environments, or for blind equalization purposes.

Finally, we show several simulation results which support the theoretically derived results.

This work has been partially supported by CAICYT grant TIC 92 # 0800-C05-01

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Authors and Affiliations

  1. ETSI Telecomunicación-UV, C/ Real de Burgos s/n, 47011, Valladolid, Spain

    Jesús Cid-Sueiro

  2. DSSR, ETSI Telecomunicación-UPM, Ciudad Universitaria, 28040, Madrid, Spain

    Jesús Cid-Sueiro, Luis Weruaga-Prieto & Aníbal R. Figueiras-Vidal

Authors
  1. Jesús Cid-Sueiro
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  2. Luis Weruaga-Prieto
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  3. Aníbal R. Figueiras-Vidal
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Editor information

José Mira Joan Cabestany Alberto Prieto

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© 1993 Springer-Verlag Berlin Heidelberg

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Cid-Sueiro, J., Weruaga-Prieto, L., Figueiras-Vidal, A.R. (1993). Optimal blind equalization of Gaussian channels. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_209

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  • DOI: https://doi.org/10.1007/3-540-56798-4_209

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56798-1

  • Online ISBN: 978-3-540-47741-9

  • eBook Packages: Springer Book Archive

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