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Data driven selection of DRX for energy efficient 5G RAN
Ericsson AB, Sweden.ORCID iD: 0000-0003-1558-4670
Ericsson AB, Sweden.
Ericsson AB, Sweden.
RISE - Research Institutes of Sweden, ICT, SICS. (Decisions, Networks and Analytics lab)ORCID iD: 0000-0002-9331-0352
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2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The number of connected mobile devices is increasing rapidly with more than 10 billion expected by 2022. Their total aggregate energy consumption poses a significant concern to society. The current 3gpp (3rd Generation Partnership Project) LTE/LTE-Advanced standard incorporates an energy saving technique called discontinuous reception (DRX). It is expected that 5G will use an evolved variant of this scheme. In general, the single selection of DRX parameters per device is non trivial. This paper describes how to improve energy efficiency of mobile devices by selecting DRX based on the traffic profile per device. Our particular approach uses a two phase data-driven strategy which tunes the selection of DRX parameters based on a smart fast energy model. The first phase involves the off-line selection of viable DRX combinations for a particular traffic mix. The second phase involves an on-line selection of DRX from this viable list. The method attempts to guarantee that latency is not worse than a chosen threshold. Alternatively, longer battery life for a device can be traded against increased latency. We built a lab prototype of the system to verify that the technique works and scales on a real LTE system. We also designed a sophisticated traffic generator based on actual user data traces. Complementary method verification has been made by exhaustive off-line simulations on recorded LTE network data. Our approach shows significant device energy savings, which has the aggregated potential over billions of devices to make a real contribution to green, energy efficient networks.

Place, publisher, year, edition, pages
Tokyo: IEEE conference proceedings, 2017.
Keywords [en]
Software architecture, 5G mobile communication, Adaptive systems, Energy efficiency, Green computing
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-33288DOI: 10.23919/CNSM.2017.8255972OAI: oai:DiVA.org:ri-33288DiVA, id: diva2:1184151
Conference
2017 13th International Conference on Network and Service Management (CNSM). 26-30 Nov. 2017. Tokyo, Japan
Available from: 2018-02-20 Created: 2018-02-20 Last updated: 2018-08-21Bibliographically approved

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Kreuger, PerSchulte, Christian

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