Adaptive Centralized Clustering Framework for Software-Defined Ultra-Dense Wireless Networks

Lyu, X; Tian, H; Ni, W; Liu, RP; Zhang, P

Adaptive Centralized Clustering Framework for Software-Defined Ultra-Dense Wireless Networks

Lyu, X

Tian, H Ni, W

Liu, RP

Zhang, P

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Vehicular Technology, 2017, 66 (9), pp. 8553 - 8557
Issue Date:: 2017-09-01

Closed Access

	Filename	Description	Size
	07874204.pdf	Published Version	463.11 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Lyu, X https://orcid.org/0000-0003-0404-4310	en_US
dc.contributor.author	Tian, H	en_US
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X	en_US
dc.contributor.author	Liu, RP https://orcid.org/0000-0001-7001-6305	en_US
dc.contributor.author	Zhang, P	en_US
dc.date.issued	2017-09-01	en_US
dc.identifier.citation	IEEE Transactions on Vehicular Technology, 2017, 66 (9), pp. 8553 - 8557	en_US
dc.identifier.issn	0018-9545	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123497
dc.description.abstract	© 2017 IEEE. This paper develops a new centralized clustering framework to mitigate strong intercell interference in a software-defined ultra-dense network, where the number of clusters can be adapting to network conditions. A directed interference graph is designed to capture the dominant interference resulting from user mobility. An asymptotically optimal Max-K-Cut method is proposed to partition the graph, achieving a ( $1-1/K$) approximation of the optimum in a polynomial time-complexity, where $K$ is the number of clusters. As a result, $K$ can be adaptively adjusted to leverage among the optimality loss, throughput, and complexity. Numerical results show that our adaptive centralized framework performs significantly better than other centralized or semidistributed clustering schemes in terms of throughput.	en_US
dc.relation.ispartof	IEEE Transactions on Vehicular Technology	en_US
dc.relation.isbasedon	10.1109/TVT.2017.2679699	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Automobile Design & Engineering	en_US
dc.title	Adaptive Centralized Clustering Framework for Software-Defined Ultra-Dense Wireless Networks	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	66	en_US
utslib.for	0902 Automotive Engineering	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	1005 Communications Technologies	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	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 - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	66	en_US

Abstract:

© 2017 IEEE. This paper develops a new centralized clustering framework to mitigate strong intercell interference in a software-defined ultra-dense network, where the number of clusters can be adapting to network conditions. A directed interference graph is designed to capture the dominant interference resulting from user mobility. An asymptotically optimal Max-K-Cut method is proposed to partition the graph, achieving a ( $1-1/K$) approximation of the optimum in a polynomial time-complexity, where $K$ is the number of clusters. As a result, $K$ can be adaptively adjusted to leverage among the optimality loss, throughput, and complexity. Numerical results show that our adaptive centralized framework performs significantly better than other centralized or semidistributed clustering schemes in terms of throughput.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/123497