Magnetic Field Model (MFM) in Soft Computing and parallelization techniques for Self Organizing Networks (SON) in Telecommunications
Self Organizing Networks (SON) requires efficient algorithms and effective real time and faster execution techniques to meet the SON requirements (use cases & desired functionalities) (as cited in Srinivasan R and Premnath K N., 2011). The essence of this journal paper is to showcase that Magnet...
Ausführliche Beschreibung
Autor*in: |
K N, Premnath [verfasserIn] R, Srinivasan [verfasserIn] Rajsingh, Elijah Blessing [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2014 |
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Umfang: |
1 Online-Ressource |
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Übergeordnetes Werk: |
Enthalten in: International journal of energy optimization and engineering - Hershey, Pa : IGI Global, 2012, 3(2014), 3, Seite 57-71 |
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Übergeordnetes Werk: |
volume:3 ; year:2014 ; number:3 ; pages:57-71 |
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DOI / URN: |
10.4018/ijeoe.2014070104 |
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Self Organizing Networks (SON) requires efficient algorithms and effective real time and faster execution techniques to meet the SON requirements (use cases & desired functionalities) (as cited in Srinivasan R and Premnath K N., 2011). The essence of this journal paper is to showcase that Magnetic Field Model (MFM) (as cited in Premnath K N et al., 2013) can be applied in prominent soft computing and parallelization techniques for SON applications, functionalities and use cases. Vast literature and practical approaches are available as part of advancements in Machine Learning, Artificial Intelligence and Fuzzy logic. Based on inspiration from nature's behavior Swarm Intelligence derived from the behaviors of Ant colony and Genetic Algorithms (Evolutionary Algorithms) are some algorithmic techniques to mention.Parallelization of MFM for centralized, hybrid SON use cases is discussed with key inspiration from Google Map Reduce (as cited in Jeffrey Dean and Sanjay Ghemawat., 2004) |
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Self Organizing Networks (SON) requires efficient algorithms and effective real time and faster execution techniques to meet the SON requirements (use cases & desired functionalities) (as cited in Srinivasan R and Premnath K N., 2011). The essence of this journal paper is to showcase that Magnetic Field Model (MFM) (as cited in Premnath K N et al., 2013) can be applied in prominent soft computing and parallelization techniques for SON applications, functionalities and use cases. Vast literature and practical approaches are available as part of advancements in Machine Learning, Artificial Intelligence and Fuzzy logic. Based on inspiration from nature's behavior Swarm Intelligence derived from the behaviors of Ant colony and Genetic Algorithms (Evolutionary Algorithms) are some algorithmic techniques to mention.Parallelization of MFM for centralized, hybrid SON use cases is discussed with key inspiration from Google Map Reduce (as cited in Jeffrey Dean and Sanjay Ghemawat., 2004) |
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Self Organizing Networks (SON) requires efficient algorithms and effective real time and faster execution techniques to meet the SON requirements (use cases & desired functionalities) (as cited in Srinivasan R and Premnath K N., 2011). The essence of this journal paper is to showcase that Magnetic Field Model (MFM) (as cited in Premnath K N et al., 2013) can be applied in prominent soft computing and parallelization techniques for SON applications, functionalities and use cases. Vast literature and practical approaches are available as part of advancements in Machine Learning, Artificial Intelligence and Fuzzy logic. Based on inspiration from nature's behavior Swarm Intelligence derived from the behaviors of Ant colony and Genetic Algorithms (Evolutionary Algorithms) are some algorithmic techniques to mention.Parallelization of MFM for centralized, hybrid SON use cases is discussed with key inspiration from Google Map Reduce (as cited in Jeffrey Dean and Sanjay Ghemawat., 2004) |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ251802116</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231205143903.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231128s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4018/ijeoe.2014070104</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ251802116</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(VZGNL)10.4018/ijeoe.2014070104</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">K N, Premnath</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Magnetic Field Model (MFM) in Soft Computing and parallelization techniques for Self Organizing Networks (SON) in Telecommunications</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Self Organizing Networks (SON) requires efficient algorithms and effective real time and faster execution techniques to meet the SON requirements (use cases & desired functionalities) (as cited in Srinivasan R and Premnath K N., 2011). The essence of this journal paper is to showcase that Magnetic Field Model (MFM) (as cited in Premnath K N et al., 2013) can be applied in prominent soft computing and parallelization techniques for SON applications, functionalities and use cases. Vast literature and practical approaches are available as part of advancements in Machine Learning, Artificial Intelligence and Fuzzy logic. Based on inspiration from nature's behavior Swarm Intelligence derived from the behaviors of Ant colony and Genetic Algorithms (Evolutionary Algorithms) are some algorithmic techniques to mention.Parallelization of MFM for centralized, hybrid SON use cases is discussed with key inspiration from Google Map Reduce (as cited in Jeffrey Dean and Sanjay Ghemawat., 2004)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Intelligent Agents</subfield><subfield code="a">Logical Agents</subfield><subfield code="a">LTE</subfield><subfield code="a">Magnetic Field Model</subfield><subfield code="a">MapReduce</subfield><subfield code="a">Parallelization</subfield><subfield code="a">Self-Organization</subfield><subfield code="a">Self Organizing Networks (SON)</subfield><subfield code="a">YARN</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">R, Srinivasan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rajsingh, Elijah Blessing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of energy optimization and engineering</subfield><subfield code="d">Hershey, Pa : IGI Global, 2012</subfield><subfield code="g">3(2014), 3, Seite 57-71</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ244418942</subfield><subfield code="w">(DE-600)2703272-3</subfield><subfield code="x">2160-9543</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:3</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:3</subfield><subfield code="g">pages:57-71</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijeoe.2014070104</subfield><subfield code="m">X:IGIG</subfield><subfield code="x">Verlag</subfield><subfield code="z">Deutschlandweit zugänglich</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijeoe.2014070104&buylink=true</subfield><subfield code="3">Abstract</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-GIS</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">3</subfield><subfield code="j">2014</subfield><subfield code="e">3</subfield><subfield code="h">57-71</subfield></datafield></record></collection>
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