Open Access

Simon Lohmüller, Fabian Rabe, Andrea Fendt, Bernhard Bauer, Lars Christoph Schmelz

• 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 costneutral 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 andAs 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 costneutral 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.…