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.