5th International ICST Conference on Performance Evaluation Methodologies and Tools

Research Article

Model For Sharing Femto Access

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  • @INPROCEEDINGS{10.4108/icst.valuetools.2011.246071,
        author={mariem krichen and johanne cohen and dominique barth},
        title={Model For Sharing Femto Access},
        proceedings={5th International ICST Conference on Performance Evaluation Methodologies and Tools},
        publisher={ICST},
        proceedings_a={VALUETOOLS},
        year={2012},
        month={6},
        keywords={game theory sharing femto access TBAS nash equilibrium distributed learning algorithm},
        doi={10.4108/icst.valuetools.2011.246071}
    }
    
  • mariem krichen
    johanne cohen
    dominique barth
    Year: 2012
    Model For Sharing Femto Access
    VALUETOOLS
    ICST
    DOI: 10.4108/icst.valuetools.2011.246071
mariem krichen1,*, johanne cohen1, dominique barth1
  • 1: prism
*Contact email: mariem.krichen@prism.uvsq.fr

Abstract

In wireless access network optimization, today’s main challenges reside in traffic offload and in the improvement of both capacity and coverage networks. The major issue for mobile operators is to find the best solution at reasonable expanses. The femto cell seems to be the answer to this problematic. In this work1 , we focus on the problem of sharing femto access between a same mobile operator’s customers. A paradigm for bandwidth sharing management added to a TBAS model for exchanging connectivity is proposed for a fair sharing connectivity system ensuring QoS. This paper focuses on an economic model based on FON model and considers the sharing femto access problem as a problem divided into to 2 levels: a game restricted to service requesters customers (SRCs) and a second game restricted to service providers customers (SPCs). We consider that SRCs are static and have some similar and regular connec- tion behavior. We also note that each SPC and each SRC have a software embedded espectively on its femto access, user equipment on which an algorithm is running to learn the best strategy increasing its gain using only local information. We will try to answer the following questions for a game with N SRCs and P SPCs: how many connections are necessary for each SRC/SPC in order to learn the strategy maximizing its gain? Does exist an algorithm converging to a stable state? If yes, does this state a Nash Equilibrium?