Graph theory and its applications to future network planning: Software-defined online small cell management

Ni, W; Collings, I; Lipman, J; Wang, X; Tao, M; Abolhasan, M

Graph theory and its applications to future network planning: Software-defined online small cell management

Ni, W

Collings, I Lipman, J

Wang, X Tao, M Abolhasan, M

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Publication Type:: Journal Article
Citation:: IEEE Wireless Communications, 2015, 22 (1), pp. 52 - 60
Issue Date:: 2015-02-01

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Field	Value	Language
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X	en_US
dc.contributor.author	Collings, I	en_US
dc.contributor.author	Lipman, J https://orcid.org/0000-0003-2877-1168	en_US
dc.contributor.author	Wang, X	en_US
dc.contributor.author	Tao, M	en_US
dc.contributor.author	Abolhasan, M https://orcid.org/0000-0002-4282-6666	en_US
dc.date.issued	2015-02-01	en_US
dc.identifier.citation	IEEE Wireless Communications, 2015, 22 (1), pp. 52 - 60	en_US
dc.identifier.issn	1536-1284	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34972
dc.description.abstract	© 2015 IEEE. Network planning is facing new and critical challenges due to ad hoc deployment, unbalanced and drastically varying traffic demands, as well as limited backhaul and hardware resources in emerging small cell architectures. We discuss the application of graph theory to address the challenges. A clique-based software-defined online network management approach is proposed that captures traffic imbalance and fluctuation of small cells and optimally plans frequencies, infrastructures, and network structure at any instant. Its applications to three important small cell scenarios of cloud radio, point-to-point microwave backhaul, and interoperator spectrum sharing are demonstrated. Comparison studies show that in each of the scenarios, this new approach is able to significantly outperform conventional static offline network planning schemes in terms of throughput and satisfaction levels of small cells with regard to allocated bandwidths. Specifically, the throughput can be improved by 155 percent for the cloud radio scenario and 110.95 percent for the microwave backhaul scenario. The satisfaction level can be improved by 40 percent for interoperator spectrum sharing.	en_US
dc.relation.ispartof	IEEE Wireless Communications	en_US
dc.relation.isbasedon	10.1109/MWC.2015.7054719	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Graph theory and its applications to future network planning: Software-defined online small cell management	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	22	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	22	en_US

Abstract:

© 2015 IEEE. Network planning is facing new and critical challenges due to ad hoc deployment, unbalanced and drastically varying traffic demands, as well as limited backhaul and hardware resources in emerging small cell architectures. We discuss the application of graph theory to address the challenges. A clique-based software-defined online network management approach is proposed that captures traffic imbalance and fluctuation of small cells and optimally plans frequencies, infrastructures, and network structure at any instant. Its applications to three important small cell scenarios of cloud radio, point-to-point microwave backhaul, and interoperator spectrum sharing are demonstrated. Comparison studies show that in each of the scenarios, this new approach is able to significantly outperform conventional static offline network planning schemes in terms of throughput and satisfaction levels of small cells with regard to allocated bandwidths. Specifically, the throughput can be improved by 155 percent for the cloud radio scenario and 110.95 percent for the microwave backhaul scenario. The satisfaction level can be improved by 40 percent for interoperator spectrum sharing.

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http://hdl.handle.net/10453/34972