Cognitive radio networks: Game modeling and self-organization using stochastic learning

Chen Hao Lin, Li Chuan Tseng, Ching-Yao Huang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Due to the high demand of spectrum utilization, cognitive radio (CR) network has been a promising solution to the problem of spectrum scarcity by using dynamic spectrum access technique. In this paper, we study one of the CR network architectures where the CR base stations (CRBSs) demand spectrum resources for the CR users to directly access and utilize. We applied an economical Cournot Game model to the system where the CRBSs are the players in this game. In order to optimize the game, we propose a stochastic learning (SL) based scheme for the CRBSs to adjust the demand amount of resources based on the action-reward history. Numerical results show the convergence toward a Nash Equilibrium (NE) point, and the system performs well in terms of the total utility comparing with other schemes.

Original languageEnglish
Title of host publication2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013
Pages3006-3010
Number of pages5
DOIs
StatePublished - 1 Dec 2013
Event2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013 - London, United Kingdom
Duration: 8 Sep 201311 Sep 2013

Publication series

NameIEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC

Conference

Conference2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, PIMRC 2013
CountryUnited Kingdom
CityLondon
Period8/09/1311/09/13

Keywords

  • Cognitive radio network
  • Cournot game
  • Stochastic learning

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