Unsupervised system for diagnosis in LTE networks using Bayesian networks

Nowadays, the size and complexity of mobile networks are growing ceaselessly. Therefore, the management of mobile networks is a significant, expensive and demanding task to perform. In order to simplify this task, Self-Organizing Networks (SON) appear as a unified solution to autonomously manage a mobile network. One of the fundamental functions of SON is self-healing. Within self-healing, the objective of fault diagnosis or root cause analysis is the identification of problem causes in faulty cells. With that aim, in this paper, an unsupervised diagnosis system for LTE (Long Term Evolution) based on Bayesian networks is presented. In particular, the system is divided in two separate steps. First of all, the discretization of the input data is done. Then, the system provides an identification of the cell status. Depending on the discretization method, the performance of the system is different, so, in this paper, different methods have been evaluated. Results have proven the high success rate achieved with the proposed system, particularly when the Expectation-Maximization (EM) algorithm is used for the discretization.