Correlation Tool for Nextgen SoN Networks

An increase in the complexity of multi-RAT networks (Radio Access Network Technologies) has resulted in numerous parameters that define and control the performance of the network. The data associated with such parameters can be harnessed to power efficient, automated and proactive network intelligence. The current paper details the development of a vital tool which works on the constitutional principles of correlation. A detailed description and the functioning of the services provided by the tool are described in the paper. The tool can be used as part of a SON solution for OAM of mobile networks replacing current procedural SON algorithms. Further, various use cases have also been delineated. A specific use case of detecting network degradation has been thoroughly implemented. The veracity of the current tool has been tested against a deep learning model which was used to predict KPI (Key Performance Indicator) degradation in a radio network data set. The results reveal that a diligent and a meticulous utilization of the proposed tool in network could drive instrumental decisions throughout the network life cycle.