A minimum spanning tree-based approach for reducing verification collisions in self-organizing networks

Abstract

The verification of Configuration Management (CM) changes has become an important step in the operation of a mobile Self-Organizing Network (SON). Typically, a verification mechanism operates in three phases. At first, it partitions the network into verification areas, then it triggers an anomaly detection algorithm for those areas, and finally generates CM undo requests for the abnormally performing ones. Those requests set the CM parameters to a previous stable state. However, verification areas may overlap and share anomalous cells which results in a verification collision. As a consequence, the verification mechanism is not able to simultaneously deploy the undo requests since there is an uncertainty which to execute and which to potentially omit. In such a case, it has to serialize the deployment process and resolve the collisions. This procedure, though, can be negatively impacted if unnecessary collisions are processed, since they might delay the execution of the queued CM undo requests. To overcome this issue, we propose an approach for changing the size of the verification areas with respect to the detected collisions. We achieve our goal by using a Minimum Spanning Tree (MST)-based clustering approach that is able to group similarly behaving cells together. Based on the group they have been assigned to, we remove cells from a verification area and prevent false positive collisions from being further processed. Furthermore, we evaluate the proposed solution in two different scenarios. First, we highlight its benefits by applying it on CM and Performance Management (PM) data collected from a real Long Term Evolution (LTE) network. Second, in a simulation study we show how it positively affects the network performance after eliminating the false positives.