Autonomas Fault Diagnosis System for Cellular Networks Based on Hidden Markov Model

Abstract

Automated diagnosis and Troubleshooting (TS) in Radio Access Networks (RAN) of cellular systems are basic management tasks, which are required to guarantee efficient use of network resources. In this paper, we investigate the usage of machine learning techniques: stochastic methods and discriminant analysis for automating these TS tasks. Our proposed framework is based on Hidden Markov Model (HMM), Principle Component Analysis (PCA) and Fisher Linear Discriminant (FLD) techniques. In a learning phase, symptoms relating to faults in the network are extracted from a network management system (NMS). Then they are used to create a fault model. This model is used to identify the unknown faults using a nearest neighbor classifier. Reported results for the automated diagnosis using live RAN measurements illustrate the efficiency of the proposed TS framework and its importance to mobile network operators.