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Fuzzy Reinforcement Learning for Dynamic Power Control in Cognitive Radio Networks

  • Jerzy Martyna
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8467)

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.

Keywords

Cognitive Radio Power Control Secondary User Cognitive Radio Network Fuzzy Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jerzy Martyna
    • 1
  1. 1.Institute of Computer Science, Faculty of Mathematics and Computer ScienceJagiellonian UniversityCracowPoland

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