SON function performance prediction in a cognitive SON management system

机译：认知SON管理系统中的SON功能性能预测

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As a reply to the increasing demand for fast mobile network connections the concept of Self-Organising Networks (SONs) has been developed, reducing the need for humans to execute Operation, Administration and Maintenance (OAM) tasks for mobile networks. However, a SON contains functions which are provided by different vendors as black boxes, making it hard to predict the performance of the network, especially under untested configurations. Since Mobile Network Operators (MNOs) have to fulfil rising mobile network performance demands while reducing costs at the same time, it is crucial to gain a better understanding of the network behaviour to allow a cost-neutral performance improvement while simultaneously reducing the risk of network misconfiguration and service disturbance. In this paper an approach is introduced to enhance SON Management models with cognitive Machine Learning (ML) methods. Therefore, the simulated behaviour of three different SON Functions is analysed and described by a Linear Regression (LR) Model. In a second step, performance data of network cells are analysed for similarities using k-Means Clustering. The findings of these two steps are then combined by fitting the models onto smaller clusters of cells. Finally, the utility of these models for predicting the performance of the network is evaluated and the different stages of refinement are compared with each other.

机译：作为对快速移动网络连接不断增长的需求的回应，已经开发了自组织网络（SON）的概念，从而减少了人们执行移动网络的运营，管理和维护（OAM）任务的需求。但是，SON包含由不同供应商提供的功能，如黑匣子，这使得很难预测网络性能，尤其是在未经测试的配置下。由于移动网络运营商（MNO）必须满足不断增长的移动网络性能要求，同时降低成本，因此，对网络行为有一个更好的了解，以在不降低成本的情况下提高性能，同时降低网络风险至关重要。配置错误和服务干扰。本文介绍了一种使用认知机器学习（ML）方法增强SON管理模型的方法。因此，通过线性回归（LR）模型分析和描述了三种不同SON函数的仿真行为。在第二步中，使用k均值聚类分析网络单元的性能数据的相似性。然后，通过将模型拟合到较小的细胞簇上来合并这两个步骤的发现。最后，评估了这些模型用于预测网络性能的效用，并将改进的不同阶段进行了比较。

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《IEEE Wireless Communications and Networking Conference Workshops》|2018年|13-18|共6页
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Simon Lohmüller; Fabian Rabe; Andrea Fendt; Bernhard Bauer; Lars Christoph Schmelz;
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Data models; Predictive models; Conferences; Testing; Measurement uncertainty; Clustering algorithms; Mathematical model;

机译：数据模型;预测模型;会议;测试;测量不确定度;聚类算法;数学模型;

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SON function performance prediction in a cognitive SON management system

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