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Conference Paper: Base station switching problem for green cellular networks with social spider algorithm

TitleBase station switching problem for green cellular networks with social spider algorithm
Authors
KeywordsGreen cellular network
Base station switching
Social spider algorithm
Evolutionary computation
Swarm intelligence
Issue Date2014
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000284
Citation
The 2014 IEEE Congress on Evolutionary Computation (CEC 2014), Beijing, China, 6-11 July 2014. In Conference Proceedings, 2014, p. 2338-2344 How to Cite?
AbstractWith the recent explosion in mobile data, the energy consumption and carbon footprint of the mobile communications industry is rapidly increasing. It is critical to develop more energy-efficient systems in order to reduce the potential harmful effects to the environment. One potential strategy is to switch off some of the under-utilized base stations during off-peak hours. In this paper, we propose a binary Social Spider Algorithm to give guidelines for selecting base stations to switch off. In our implementation, we use a penalty function to formulate the problem and manage to bypass the large number of constraints in the original optimization problem. We adopt several randomly generated cellular networks for simulation and the results indicate that our algorithm can generate superior performance. © 2014 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/217398
ISBN

 

DC FieldValueLanguage
dc.contributor.authorYu, JJ-
dc.contributor.authorLi, VOK-
dc.date.accessioned2015-09-18T05:58:24Z-
dc.date.available2015-09-18T05:58:24Z-
dc.date.issued2014-
dc.identifier.citationThe 2014 IEEE Congress on Evolutionary Computation (CEC 2014), Beijing, China, 6-11 July 2014. In Conference Proceedings, 2014, p. 2338-2344-
dc.identifier.isbn978-1-4799-1488-3-
dc.identifier.urihttp://hdl.handle.net/10722/217398-
dc.description.abstractWith the recent explosion in mobile data, the energy consumption and carbon footprint of the mobile communications industry is rapidly increasing. It is critical to develop more energy-efficient systems in order to reduce the potential harmful effects to the environment. One potential strategy is to switch off some of the under-utilized base stations during off-peak hours. In this paper, we propose a binary Social Spider Algorithm to give guidelines for selecting base stations to switch off. In our implementation, we use a penalty function to formulate the problem and manage to bypass the large number of constraints in the original optimization problem. We adopt several randomly generated cellular networks for simulation and the results indicate that our algorithm can generate superior performance. © 2014 IEEE.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000284-
dc.relation.ispartofCongress on Evolutionary Computation (CEC)-
dc.rightsCongress on Evolutionary Computation (CEC). Copyright © IEEE.-
dc.rights©2014 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.-
dc.subjectGreen cellular network-
dc.subjectBase station switching-
dc.subjectSocial spider algorithm-
dc.subjectEvolutionary computation-
dc.subjectSwarm intelligence-
dc.titleBase station switching problem for green cellular networks with social spider algorithm-
dc.typeConference_Paper-
dc.identifier.emailYu, JJ: jqyu@eee.hku.hk-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityLi, VOK=rp00150-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1109/CEC.2014.6900235-
dc.identifier.scopuseid_2-s2.0-84908568349-
dc.identifier.hkuros254364-
dc.identifier.spage2338-
dc.identifier.epage2344-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 151119-

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