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Performance evaluation of prefetching algorithm for real-time edge content delivery in 5G system
Tokyo Institute of Technology, Japan.
Tokyo Institute of Technology, Japan.
Mm-Wave Group, Germany.
Mm-Wave Group, Germany.
Show others and affiliations
2019 (English)In: IEEE Vehicular Technology Conference, IEEE, 2019, article id 8891130Conference paper, Published paper (Refereed)
Abstract [en]

Recently, high-density deployment of mmWave small cell base stations has been focused as a means to cope with the rapidly increasing mobile data traffic. MmWave overlay heterogeneous network (HetNet) is proposed to realize eMBB, however laying cost of backbone utilizing high-capacity optical fibers is very costly to support ultra- broadband accesses. On the other hand, Multi- access Edge Computing (MEC) is proposed to reduce mobile data traffic on backhaul networks. MEC deploys contents requested by User Equipment (UE) nearby in advance based on registered context information e.g. location, destination, and required contents to the orchestrator. Therefore, it is possible to utilize mmWave access under existing backhaul networks by introducing prefetching algorithm. The architecture is enabled by wireless Software Defined Network (SDN) technology. In this paper, Proof-of-Concept (PoC) architecture is proposed, and the effectiveness of mmWave HetNet with MEC utilizing prefetching algorithm is confirmed by measuring the download time of real content.

Place, publisher, year, edition, pages
IEEE, 2019. article id 8891130
Keywords [en]
5G, HetNet, MEC, MmWave, Orchestration, PoC, Prefetching algorithm, SDN, Testbed, Heterogeneous networks, Millimeter waves, Network architecture, Optical fibers, Testbeds, Backhaul networks, Context information, Heterogeneous Network (HetNet), mm-Wave, Mobile data traffic, 5G mobile communication systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-76478DOI: 10.1109/VTCFall.2019.8891130Scopus ID: 2-s2.0-85075237221ISBN: 9781728112206 (print)OAI: oai:DiVA.org:kau-76478DiVA, id: diva2:1388099
Conference
90th IEEE Vehicular Technology Conference, VTC 2019 Fall, 22 September 2019 through 25 September 2019
Available from: 2020-01-23 Created: 2020-01-23 Last updated: 2020-08-07Bibliographically approved

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Santos, Ricardo

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CiteExportLink to record
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Citation style
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