Extracting cell patterns from high-dimensional radio network performance datasets using self-organizing maps and k-means clustering

Abstract

Mobile Radio Networks produces many of Operations, Administration, and Maintenance (OAM) data used by operators for network operational assurance. These data include multiple and diverse performance measurements and indicators that characterize the behavior of the radio cells. Being able to properly cluster the apparently dissimilar behaviors exhibited by a large number of individual cells into a reduced set of prototype patterns constitutes a valuable tool to support multiple processes such as cell configuration optimization or fault performance root cause analysis. While powerful clustering methods such as Self Organized Maps (SOM) exist, there is practically no literature showing the applicability of these methods of OAM datasets with a high number of attributes (>20) collected from live network deployments. Moreover, the applicability of the clustering methods does not come free of open questions since, for instance, when using SOM there is no explicitly obtained information about clusters after the SOM training in the underlying data, so the $k$ -means technique for grouping SOM units has to be applied afterward. In this context, this paper describes a methodology to cluster radio cells based on a combination of SOM and K-means methods. The methodology is applied to extract cell patterns of the characterization of the long-term behavior (15 days' observation period) and short-term behavior (hourly observation periods) of mobile cells. OAM datasets collected from a live 4G/LTE network deployed in a major European city are used in the analysis.