LTE performance data reduction for knowledge acquisition

In the last years, mobile networks have seen a great increase in complexity, as the data traffic, the demand for quality and the variety of offered services have grown. The management costs of modern networks are growing, at the same time as operators compete to offer shorter downtime and less impact of network issues on the user experience. Self-Organizing Networks (SON) offer a solution to these problems. Among these SON functionalities in cellular networks, self-healing automates the resolution of problems in the radio access network. To perform the task of diagnosis (or root cause analysis), Knowledge-Based Systems (KBS) are often used. These systems need a previous process of training (or learning), in which they are fed instances of real problems. In this paper, an algorithm for extracting the key information for these vectors is proposed. The inputs of the algorithm are big matrices of time-dependent network performance data, and the outputs are simple one-dimensional vectors ready to be used in learning algorithms.