On cellular network channels data mining and decision making through ant colony optimization and multi agent systems strategies

P. Papazoglou, D. Karras, Rallis Papademetriou

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


    Finding suitable channels to allocate in order to serve increasing user demands in a cellular network, which is a dynamical system, constitute the most important issue in terms of network performance since they define the bandwidth management methodology. In modern cellular networks these strategies become challenging issues especially when advanced services are applied. The effectiveness of decision making for channel allocation in a cellular network is strongly connected to current traffic and wireless environment conditions. Moreover, in large scale environments, network states change dynamically and the network performance prediction is a hard task. In the recent literature, the network adaptation to current real user needs seems it could be achieved through computational intelligence based channel allocation schemes mainly involving genetic algorithms. In this paper, a quite new approach for communication channels decision making, based on ant colony optimization, which is a special form of swarm intelligence, modelled through multi agent methodology is presented. The main novelty of this research lies on modelling this optimization scheme through multi agent systems. The simulation model architecture which includes network and ant agents are also presented as well as the performance results based on the above techniques. Finally, the current study, also, shows that there is a great field of research concerning intelligent techniques modelled through multi-agent methodologies focused on channels decision making and bandwidth management in wireless communication systems.
    Original languageEnglish
    Title of host publicationAdvances in data mining: applications and theoretical aspects
    Subtitle of host publication9th Industrial Conference, ICDM 2009, Leipzig, Germany, July 20 - 22, 2009. Proceedings
    EditorsP. Perner
    Place of PublicationBerlin
    Number of pages16
    ISBN (Electronic)978-3-642-03067-3
    ISBN (Print)978-3-642-03066-6
    Publication statusPublished - 2009
    Event9th Industrial Conference on Data Mining (ICDM) 2009 - Leipzig, Germany
    Duration: 6 Dec 20099 Dec 2009

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference9th Industrial Conference on Data Mining (ICDM) 2009


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