3rd International ICSTConference on Wireless Internet

Research Article

What a Cognitive Radio Network Could Learn From a School of Fish

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  • @INPROCEEDINGS{10.4108/wicon.2007.2299,
        author={Christian Doerr and Douglas C. Sicker and Dirk Grunwald},
        title={What a Cognitive Radio Network Could Learn From a School of Fish},
        proceedings={3rd International ICSTConference on Wireless Internet},
        proceedings_a={WICON},
        year={2010},
        month={5},
        keywords={Cognitive radio networks Biologically-inspired algorithms Emergent behavior Swarm},
        doi={10.4108/wicon.2007.2299}
    }
    
  • Christian Doerr
    Douglas C. Sicker
    Dirk Grunwald
    Year: 2010
    What a Cognitive Radio Network Could Learn From a School of Fish
    WICON
    ICST
    DOI: 10.4108/wicon.2007.2299
Christian Doerr1,*, Douglas C. Sicker1,*, Dirk Grunwald1,*
  • 1: Dept. of Computer Science University of Colorado at Boulder Boulder, CO, USA
*Contact email: Christian.Doerr@colorado.edu, Douglas.Sicker@colorado.edu, Dirk.Grunwald@colorado.edu

Abstract

In recent years, various types of control algorithms have been proposed for cognitive radios (CR), ranging from algorithms coordinated by centralized control to ones coordinated in a distributed manner. These algorithms, however, all require communication to either peer nodes or a master node, thus creating communication overhead and potential vulnerability. We introduce a new class of control algorithms to the area of CRs derived from observations of emergent design in nature. Specifically, we introduce an algorithmic approach based on swarm behavior to the task of configuration management in CR networks. Without requiring the exchange of information among peers or a central authority, CRs equipped with such an algorithm are able to globally optimize the configuration of a CR network in the presence of interference and jammers, while only relying on local information, thus providing a fast and efficient way for configuration management especially for large networks.