Power management in LTE networks by applying intelligent agents

Lin, K; Debenham, JK

Power management in LTE networks by applying intelligent agents

Lin, K Debenham, JK

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2011 6th International Conference on Broadband and Biomedical Communications (IB2Com), 2011, pp. 163 - 166
Issue Date:: 2011-01

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Field	Value	Language
dc.contributor.author	Lin, K	en_US
dc.contributor.author	Debenham, JK	en_US
dc.contributor.editor	NA	en_US
dc.date	2011-11-21	en_US
dc.date.issued	2011-01	en_US
dc.identifier.citation	2011 6th International Conference on Broadband and Biomedical Communications (IB2Com), 2011, pp. 163 - 166	en_US
dc.identifier.isbn	978-1-4673-0768-0	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19171
dc.description.abstract	The improvement of energy efficiency in wireless cellular networks has become a popular topic in recent years due to its positive effects on the environment and economical benefits for network operators. An effective way to improve energy efficiency is to deploy energy-efficient base stations and turn as many base stations off as possible. In this paper, we consider an LTE network composed by the new base stations invented by Alcatel-Lucent Light Radio Cubes (cube), and propose a power management scheme. The power management scheme assumes that each cube has an intelligent agent (IA), and through mining network traffic, each IA requests its associated cube to turn on or off, adjust radiation power, or change beam direction. Through extensive experiments, we find that this IA-based power management scheme is able to save more than 50% power while not deteriorating network performance	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	2011 6th International Conference on Broadband and Biomedical Communications (IB2Com)	en_US
dc.relation.ispartof	International Conference on Broadband and Biomedical Communications	en_US
dc.relation.isbasedon	10.1109/IB2Com.2011.6217913	en_US
dc.rights	© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Power management in LTE networks by applying intelligent agents	en_US
dc.type	Conference Proceeding
utslib.location	Piscataway, USA	en_US
utslib.location.activity	Melbourne, Australia	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Melbourne, Australia	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 Software
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Piscataway, USA	en_US
pubs.start-date	2011-11-21	en_US

Abstract:

The improvement of energy efficiency in wireless cellular networks has become a popular topic in recent years due to its positive effects on the environment and economical benefits for network operators. An effective way to improve energy efficiency is to deploy energy-efficient base stations and turn as many base stations off as possible. In this paper, we consider an LTE network composed by the new base stations invented by Alcatel-Lucent Light Radio Cubes (cube), and propose a power management scheme. The power management scheme assumes that each cube has an intelligent agent (IA), and through mining network traffic, each IA requests its associated cube to turn on or off, adjust radiation power, or change beam direction. Through extensive experiments, we find that this IA-based power management scheme is able to save more than 50% power while not deteriorating network performance

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http://hdl.handle.net/10453/19171