A multi-objective approach to indoor wireless heterogeneous networks planning based on biogeography-based optimization
In this paper, we present a multi-objective optimization approach for indoor wireless network planning subject to constraints for exposure minimization, coverage maximization and power consumption minimization. We consider heterogeneous networks consisting of WiFi access points (APs) and long term e...
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| Veröffentlicht in: | Computer networks (Amsterdam, Netherlands : 1999) Jg. 91; S. 564 - 576 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Amsterdam
Elsevier B.V
14.11.2015
Elsevier Sequoia S.A |
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| ISSN: | 1389-1286, 1872-7069 |
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| Abstract | In this paper, we present a multi-objective optimization approach for indoor wireless network planning subject to constraints for exposure minimization, coverage maximization and power consumption minimization. We consider heterogeneous networks consisting of WiFi access points (APs) and long term evolution (LTE) femtocells. We propose a design framework based on multi-objective biogeography-based optimization (MOBBO). We apply the MOBBO algorithm to network planning design cases in a real office environment. To validate this approach we compare results with other multi-objective algorithms like the nondominated sorting genetic algorithm-II (NSGA-II) and the generalized differential evolution (GDE3) algorithm. The results of the proposed method indicate the advantages and applicability of the multi-objective approach. |
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| AbstractList | In this paper, we present a multi-objective optimization approach for indoor wireless network planning subject to constraints for exposure minimization, coverage maximization and power consumption minimization. We consider heterogeneous networks consisting of WiFi access points (APs) and long term evolution (LTE) femtocells. We propose a design framework based on multi-objective biogeography-based optimization (MOBBO). We apply the MOBBO algorithm to network planning design cases in a real office environment. To validate this approach we compare results with other multi-objective algorithms like the nondominated sorting genetic algorithm-II (NSGA-II) and the generalized differential evolution (GDE3) algorithm. The results of the proposed method indicate the advantages and applicability of the multi-objective approach. |
| Author | Martens, Luc Liu, Ning Plets, David Joseph, Wout Goudos, Sotirios K. |
| Author_xml | – sequence: 1 givenname: Sotirios K. orcidid: 0000-0001-5981-5683 surname: Goudos fullname: Goudos, Sotirios K. email: sgoudo@physics.auth.gr, sgoudos@gmail.com organization: Radiocommunications Laboratory, Department of Physics, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece – sequence: 2 givenname: David surname: Plets fullname: Plets, David organization: WiCa, Ghent University/iMinds, Department of Information Technology, Gaston Crommenlaan∼8∼box∼201, B-9050 Ghent, Belgium – sequence: 3 givenname: Ning surname: Liu fullname: Liu, Ning organization: WiCa, Ghent University/iMinds, Department of Information Technology, Gaston Crommenlaan∼8∼box∼201, B-9050 Ghent, Belgium – sequence: 4 givenname: Luc surname: Martens fullname: Martens, Luc organization: WiCa, Ghent University/iMinds, Department of Information Technology, Gaston Crommenlaan∼8∼box∼201, B-9050 Ghent, Belgium – sequence: 5 givenname: Wout surname: Joseph fullname: Joseph, Wout organization: WiCa, Ghent University/iMinds, Department of Information Technology, Gaston Crommenlaan∼8∼box∼201, B-9050 Ghent, Belgium |
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| Cites_doi | 10.1109/74.789733 10.1109/TWC.2011.091411.101899 10.1109/74.924603 10.1109/4235.996017 10.1109/NAPS.2009.5484010 10.1109/TAP.2013.2254437 10.1109/TAP.2009.2026714 10.1109/LCOMM.2010.120610.101741 10.1109/TWC.2013.052213.121751 10.1007/11844297_56 10.1023/A:1008202821328 10.1109/TVT.2011.2151215 10.1109/TWC.2009.080939 10.1109/TAP.2009.2032100 10.1109/TWC.2011.051311.100956 10.1109/TWC.2011.101210.101698 10.1109/TPWRS.2009.2034525 10.1109/EuCAP.2012.6206314 10.1109/TNB.2013.2294716 10.1016/j.asoc.2011.12.020 10.1109/TWC.2011.110811.110220 10.1109/TWC.2009.090792 10.1186/1687-1499-2012-340 10.1109/TWC.2013.080113.121280 10.2528/PIER10052302 10.1007/978-3-642-38703-6_38 10.1109/TIM.2011.2135030 10.1007/s00158-003-0368-6 10.1109/TEVC.2003.810758 10.2528/PIER13013003 10.1109/MCI.2008.919050 10.1109/TEVC.2008.919004 10.1016/j.comcom.2011.03.008 10.1097/HP.0b013e31822f8e39 10.2528/PIER12061301 10.1109/LCOMM.2011.022411.101804 |
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| Keywords | Pareto optimization Indoor wireless network planning Exposure minimization Heterogeneous networks Multi-objective optimization Biogeography-based optimization |
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| SubjectTerms | Algorithms Biogeography Biogeography-based optimization Biological evolution Cellular communication Classification Design optimization Energy consumption Evolution Evolutionary algorithms Exposure minimization Genetic algorithms Genetics Heterogeneous networks Indoor Indoor wireless network planning Maximization Minimization Multi-objective optimization Multiple objective analysis Networks Objectives Optimization Optimization algorithms Pareto optimization Planning Power consumption Sorting algorithms Studies Wireless communication systems Wireless networks |
| Title | A multi-objective approach to indoor wireless heterogeneous networks planning based on biogeography-based optimization |
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