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Energy Saving Game for Massive MIMO: Coping With Daily Load Variation
Aalto Univ, Finland; KTH Royal Inst Technol, Sweden.
KTH Royal Inst Technol, Sweden.
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
Aalto Univ, Finland.
2018 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 67, no 3, p. 2301-2313Article in journal (Refereed) Published
Abstract [en]

Massive MIMO (MM) is one of the leading technologies that can cater for very high capacity demand. However, energy consumption of MM systems needs to be load adaptive in order to cope with the significant temporal load variations (TLV) over a day. In this paper, we propose a game-theoretic model for studying load adaptive multicell massive MIMO system where each base station (BS) adapts the number of antennas to the TLV in order to maximize the downlink energy efficiency (EE). The utility function considered here is defined as the number of bits transferred per Joule of energy. In order to incorporate the TLV, the load at each BS is modeled as an M/G/m/m state dependent queue under the assumption that the network is dimensioned to serve a maximum number of users at the peak load. The EE maximization problem is formulated in a game theoretic framework where the number of antennas to be used by a BS is determined through the best response iteration. This load adaptive system achieves around 24% higher EE and saves around 40% energy compared to a baseline system where the BSs always run with the fixed number of antennas that is most energy efficient at the peak load and that can be switched OFF when there is no traffic.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2018. Vol. 67, no 3, p. 2301-2313
Keywords [en]
Massive MIMO; energy efficiency; M/G/m/m queue; game theory
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-147435DOI: 10.1109/TVT.2017.2769163ISI: 000427863000038OAI: oai:DiVA.org:liu-147435DiVA, id: diva2:1206536
Note

Funding Agencies|EIT ICT Labs; Academy of Finland; ELLIIT; Swedish Foundation for Strategic Research project

Available from: 2018-05-17 Created: 2018-05-17 Last updated: 2018-05-17

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