6th International ICST Symposium on Modeling and Optimization

Research Article

A Markovian Model for Mobile Cellular Networks with QoS Differentiation

Download565 downloads
  • @INPROCEEDINGS{10.4108/ICST.WIOPT2008.2988,
        author={Georges Nogueira and Bruno Baynat and Ahmed Ziram},
        title={A Markovian Model for Mobile Cellular Networks with QoS Differentiation},
        proceedings={6th International ICST Symposium on Modeling and Optimization},
        publisher={IEEE},
        proceedings_a={WIOPT},
        year={2008},
        month={8},
        keywords={Analytical models  Circuits  GSM  Ground penetrating radar  Land mobile radio cellular systems  Performance analysis  Quality of service Radio link Telecommunication traffic Traffic control},
        doi={10.4108/ICST.WIOPT2008.2988}
    }
    
  • Georges Nogueira
    Bruno Baynat
    Ahmed Ziram
    Year: 2008
    A Markovian Model for Mobile Cellular Networks with QoS Differentiation
    WIOPT
    IEEE
    DOI: 10.4108/ICST.WIOPT2008.2988
Georges Nogueira1,*, Bruno Baynat1,*, Ahmed Ziram2,*
  • 1: Universite Pierre et Marie Curie, Paris6, Paris, FRANCE.
  • 2: Nortel, Chateaufort, FRANCE.
*Contact email: georges.nogueira@lip6.fr, bruno.baynat@lip6.fr, aziram@nortel.com

Abstract

This paper presents a realistic and accurate analytical model to dimension mobile cellular networks with QoS differentiation. QoS per applicative flow is commonly defined in GPRS/EDGE or 3G systems where streaming applications with real time properties and elastic data applications have to share radio resources. The need for accurate and fast-computing tools is of primary importance to tackle complex and exhaustive dimensioning issues. In this paper, we present a generic QoS analytical model developed in the context of EDGE networks but that can be adapted to a different technology. We develop a Markovian model that takes into account the QoS differentiation between real time and non-real time classes and gives expressions for all the required performance parameters. We compare our model with simulation and show its accuracy.