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Fuzzy Reinforcement Learning for Dynamic Power Control in Cognitive Radio Networks

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Artificial Intelligence and Soft Computing (ICAISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8467))

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Abstract

Intelligent and flexible spectrum access procedures and resource allocation methods are needed to build cognitive radio (CR) networks. Apart from the major objective to maximise spectra efficiency, the goal of the CR network design is to rationalise the distribution of radio resources and the cost of their usage. This paper proposes a new fuzzy reinforcement learning method that allows for learning the best transmit power control strategy that in turn enables cognitive secondary users to achieve its required transmission rate and quality whilst minimising interference. An example is presented to illustrate the performance and applicability of the proposed method.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Faculty of Mathematics and Computer Science, Jagiellonian University, ul. Prof. S. Lojasiewicza 6, 30-348, Cracow, Poland

    Jerzy Martyna

Authors
  1. Jerzy Martyna
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Computer Science Division, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2014 Springer International Publishing Switzerland

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Martyna, J. (2014). Fuzzy Reinforcement Learning for Dynamic Power Control in Cognitive Radio Networks. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_21

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  • DOI: https://doi.org/10.1007/978-3-319-07173-2_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07172-5

  • Online ISBN: 978-3-319-07173-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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