软计算中的磁场模型(MFM)和电信自组织网络(SON)的并行化技术

Premnath K N, Srinivasan R, E. Rajsingh
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引用次数: 3

摘要

自组织网络SON需要有效的算法和有效的实时和更快的执行技术,以满足SON需求用例和所需的功能,如Srinivasan R和Premnath K N., 2011所引用的。这篇期刊论文的本质是展示了Premnath K N et al., 2013中引用的磁场模型MFM可以应用于SON应用程序、功能和用例的突出软计算和并行化技术。作为机器学习、人工智能和模糊逻辑的进步的一部分,大量的文献和实用方法是可用的。基于自然行为的启发,从蚁群行为衍生出的群体智能和遗传算法,进化算法是一些值得提及的算法技术。在集中的、混合的SON用例中讨论了MFM的并行化,主要灵感来自于Jeffrey Dean和Sanjay Ghemawat引用的谷歌Map Reduce。, 2004年。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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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