Cell Splitting for Energy-Efficient Massive MIMO

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    34 Downloads (Pure)

    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)
    PublisherInstitute of Electrical and Electronic Engineers IEEE
    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

    Fingerprint

    Energy Efficient
    Multiple-input multiple-output (MIMO)
    Cell
    Energy efficiency
    Energy Efficiency
    Power transmission
    Base stations
    Antenna
    Receiving antennas
    Multiple-input multiple-output (MIMO) Systems
    Uplink
    Adaptive Systems
    Search Space
    Optimization Algorithm
    Antennas
    Maximise
    Optimise
    Target

    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) Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/VTCFall.2017.8288110
    Apilo, Olli ; Lasanen, Mika ; Wang, Jiaheng ; Mämmelä, Aarne. / Cell Splitting for Energy-Efficient Massive MIMO. 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall). Institute of Electrical and Electronic Engineers IEEE, 2018.
    @inproceedings{b9198a0da01e4bdcae0b9fbbfac5a71f,
    title = "Cell Splitting for Energy-Efficient Massive MIMO",
    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.",
    keywords = "5G, Energy Efficiency, Load Adaptive Base Station, Massive Mimo, User Scheduling",
    author = "Olli Apilo and Mika Lasanen and Jiaheng Wang and Aarne M{\"a}mmel{\"a}",
    note = "Project code: 102085",
    year = "2018",
    doi = "10.1109/VTCFall.2017.8288110",
    language = "English",
    isbn = "978-1-5090-5936-2",
    booktitle = "2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)",
    publisher = "Institute of Electrical and Electronic Engineers IEEE",
    address = "United States",

    }

    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). Institute of Electrical and Electronic Engineers IEEE, IEEE 86th Vehicular Technology Conference, VTC-Fall 2017, Toronto, Canada, 24/09/17. https://doi.org/10.1109/VTCFall.2017.8288110

    Cell Splitting for Energy-Efficient Massive MIMO. / Apilo, Olli; Lasanen, Mika; Wang, Jiaheng; Mämmelä, Aarne.

    2017 IEEE 86th Vehicular Technology Conference (VTC-Fall). Institute of Electrical and Electronic Engineers IEEE, 2018.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    TY - GEN

    T1 - Cell Splitting for Energy-Efficient Massive MIMO

    AU - Apilo, Olli

    AU - Lasanen, Mika

    AU - Wang, Jiaheng

    AU - Mämmelä, Aarne

    N1 - Project code: 102085

    PY - 2018

    Y1 - 2018

    N2 - 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.

    AB - 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.

    KW - 5G

    KW - Energy Efficiency

    KW - Load Adaptive Base Station

    KW - Massive Mimo

    KW - User Scheduling

    UR - http://www.scopus.com/inward/record.url?scp=85045250578&partnerID=8YFLogxK

    U2 - 10.1109/VTCFall.2017.8288110

    DO - 10.1109/VTCFall.2017.8288110

    M3 - Conference article in proceedings

    SN - 978-1-5090-5936-2

    BT - 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)

    PB - Institute of Electrical and Electronic Engineers IEEE

    ER -

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