mca 14(1): e4

Research Article

Cell Identification based on Received Signal Strength Fingerprints: Concept and Application towards Energy Saving in Cellular Networks

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  • @ARTICLE{10.4108/mca.1.4.e4,
        author={Elke Roth-Mandutz and Stephen S. Mwanje and Andreas Mitschele-Thiel},
        title={Cell Identification based on Received Signal Strength Fingerprints: Concept and Application towards Energy Saving in Cellular Networks},
        journal={EAI Endorsed Transactions on Mobile Communications and Applications},
        volume={1},
        number={1},
        publisher={ICST},
        journal_a={MCA},
        year={2014},
        month={9},
        keywords={Cell identification, cellular network, energy saving, fingerprinting, heterogeneous networks, localization, Long Term Evolution (LTE), Self-Organizing Networks (SON), small cells},
        doi={10.4108/mca.1.4.e4}
    }
    
  • Elke Roth-Mandutz
    Stephen S. Mwanje
    Andreas Mitschele-Thiel
    Year: 2014
    Cell Identification based on Received Signal Strength Fingerprints: Concept and Application towards Energy Saving in Cellular Networks
    MCA
    ICST
    DOI: 10.4108/mca.1.4.e4
Elke Roth-Mandutz1,*, Stephen S. Mwanje1, Andreas Mitschele-Thiel1
  • 1: Ilmenau University of Technology, Ilmenau, Germany
*Contact email: elke.roth-mandutz@tu-ilmenau.de

Abstract

The increasing deployment of small cells aimed at off-loading data traffic from macrocells in heterogeneous networks has resulted in a drastic increase in energy consumption in cellular networks. Energy consumption can be optimized in a selforganized way by adapting the number of active cells in response to the current traffic demand. In this paper we concentrate on the complex problem of how to identify small cells to be reactivated in situations where multiple cells are concurrently inactive. Solely based on the received signal strength, we present cell-specific patterns for the generation of unique cell fingerprints. The cell fingerprints of the deactivated cells are matched with measurements from a high data rate demanding mobile device to identify the most appropriate candidate. Our scheme results in a matching success rate of up to 100% to identify the best cell depending on the number of cells to be activated.