Automated diagnosis for UMTS networks using Bayesian network approach

This paper presents an automated diagnosis in troubleshooting (TS) for Universal Mobile Telecommunications System (UMTS) networks using a Bayesian network (BN) approach. An automated diagnosis model is first described using the Naive Bayesian Classifier. To increase the performance of the diagnosis model, the entropy minimization discretization (EMD) method is incorporated into the model to select optimal segments for the discretization of the input symptoms. In the first phase, the diagnosis model is constructed using a dynamic simulator. The simulator TS platform allows generation of a large amount of data required to study the relations between faults and symptoms. In the second phase, the diagnosis model is adapted to a real UMTS network using counters and key performance indicators (KPIs) recovered from an Operations and Maintenance Center (OMC). Results for the automated diagnosis using both network simulator and real UNITS network measurements illustrate the efficiency of the proposed TS approach and its importance to mobile network operators.