1st International Conference on 5G for Ubiquitous Connectivity

Research Article

Prediction of Channel Quality after Handover for Mobility Management in 5G

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  • @INPROCEEDINGS{10.4108/icst.5gu.2014.258140,
        author={Zdenek Becvar and Pavel Mach and Emilio Calvanese Strinati},
        title={Prediction of Channel Quality after Handover for Mobility Management in 5G},
        proceedings={1st International Conference on 5G for Ubiquitous Connectivity},
        publisher={IEEE},
        proceedings_a={5GU},
        year={2014},
        month={12},
        keywords={5g call admission control handover interference prediction mobility},
        doi={10.4108/icst.5gu.2014.258140}
    }
    
  • Zdenek Becvar
    Pavel Mach
    Emilio Calvanese Strinati
    Year: 2014
    Prediction of Channel Quality after Handover for Mobility Management in 5G
    5GU
    IEEE
    DOI: 10.4108/icst.5gu.2014.258140
Zdenek Becvar1,*, Pavel Mach1, Emilio Calvanese Strinati2
  • 1: Czech Technical University in Prague
  • 2: CEA-Leti
*Contact email: zdenek.becvar@fel.cvut.cz

Abstract

The fifth generation of wireless networks should enable the same experience to users at home, in the office or on the move thanks to seamless handover. Call admission control (CAC) provides the means to avoid call drops due to lack of resources at a target cell during handover. The purpose of the CAC is to decide if handover should be initiated or if a new call can be established. A specific quantity of resources is reserved to the users entering the cell in the future to avoid call drops. A prediction of user's movement and amount of resources required by the users after handover can be performed in order to optimize amount of reserved resources. In this paper, we address prediction of the number of resources required by the users at the target cell after handover. To that end, we propose new approach for prediction of channel quality indicator (CQI) after handover. The prediction exploits knowledge of handover hysteresis and decomposition of interference into two parts. As the results show, the proposed algorithm increases ratio of successfully predicted CQI up to 1.9 times with respect to existing approaches.