Cell Splitting for Energy-Efficient Massive MIMO

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    Abstract

    In this paper, we propose a novel cell splitting approach for massive multiple-input multiple-output (MIMO) base stations to improve energy efficiency. The user equipments (UEs) in the cell are divided into two groups based on their distances to the base station. These two UE groups are scheduled at different time slots, which effectively splits a cell into inner and outer cells. The number of transmitting and receiving antennas together with the downlink and uplink transmission powers are adapted according to the number of cell edge and center UEs to maximize energy efficiency. We propose two algorithms to optimize the number of antennas and transmission powers. Cell splitting is able to reach energy efficiency (EE) gain of 11-41 % depending on the UE density when compared to a conventional load-adaptive massive MIMO system. The inevitable loss of cell edge UE rates can be controlled by setting a target UE rate, which also reduces the search space of the optimization algorithm.
    Original languageEnglish
    Title of host publication2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Number of pages6
    ISBN (Electronic)978-1-5090-5935-5, 978-1-5090-5934-8
    ISBN (Print)978-1-5090-5936-2
    DOIs
    Publication statusPublished - 2018
    MoE publication typeNot Eligible
    EventIEEE 86th Vehicular Technology Conference, VTC-Fall 2017 - Toronto, Canada
    Duration: 24 Sep 201727 Sep 2017

    Conference

    ConferenceIEEE 86th Vehicular Technology Conference, VTC-Fall 2017
    Abbreviated titleVTC-Fall 2017
    CountryCanada
    CityToronto
    Period24/09/1727/09/17

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    Keywords

    • 5G
    • Energy Efficiency
    • Load Adaptive Base Station
    • Massive Mimo
    • User Scheduling

    Cite this

    Apilo, O., Lasanen, M., Wang, J., & Mämmelä, A. (2018). Cell Splitting for Energy-Efficient Massive MIMO. In 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall) IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/VTCFall.2017.8288110