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...
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Gespeichert in:
| 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 |
|---|---|
| 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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R, Srinivasan Rajsingh, Elijah Blessing |
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R, Srinivasan Rajsingh, Elijah Blessing |
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| doi_str |
10.4018/ijeoe.2014070104 |
| up_date |
2024-07-06T11:38:56.136Z |
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1803829574250790912 |
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7.399455 |