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Least-Squares Support Vector Machines for Scheduling Transmission in Wireless Networks

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Computational Intelligence, Theory and Applications

Part of the book series: Advances in Soft Computing ((AINSC,volume 33))

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Abstract

For the scheduling transmission over a fading channel in wireless networks, the performance increases significantly if a specialized packet scheduler is used. The properties of this scheduler demand a learning mechanism. For this purpose, a least squares support vector machine (LS-SVM) is proposed as the learning mechanism. In the SVM methodology the number of the unknown can be infinitely dimensional. The given method is illustrated by some numerical examples.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Jagiellonian University, Nawojki 11, Cracow, Poland

    Jerzy Martyna

Authors
  1. Jerzy Martyna
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Editor information

Editors and Affiliations

  1. Department of Computer Science 1, University of Dortmund, FB 8 Ektrotechnik FB Informatik LS Informatik I, Postfach 50 05 00, 44221, Dortmund, Germany

    Bernd Reusch

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© 2005 Springer-Verlag Berlin Heidelberg

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Martyna, J. (2005). Least-Squares Support Vector Machines for Scheduling Transmission in Wireless Networks. In: Reusch, B. (eds) Computational Intelligence, Theory and Applications. Advances in Soft Computing, vol 33. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31182-3_56

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  • DOI: https://doi.org/10.1007/3-540-31182-3_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22807-3

  • Online ISBN: 978-3-540-31182-9

  • eBook Packages: EngineeringEngineering (R0)

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