Automated RRM Optimization of LTE networks using Statistical Learning

The mobile telecommunication industry has experienced a very rapid growth in the recent past. This has resulted in significant technological and architectural evolution in the wireless networks. The expansion and the heterogenity of these networks have made their operational cost more and more important. Typical faults in these networks may be related to equipment breakdown and inappropriate planning and configuration. In this context, automated troubleshooting in wireless networks receives a growing importance, aiming at reducing the operational cost and providing high-quality services for the end-users. Automated troubleshooting can reduce service breakdown time for the clients, resulting in the decrease in client switchover to competing network operators. The Radio Access Network (RAN) of a wireless network constitutes its biggest part. Hence, the automated troubleshooting of RAN of the wireless networks is very important. The troubleshooting comprises the isolation of the faulty cells (fault detection), identifying the causes of the fault (fault diagnosis) and the proposal and deployement of the healing action (solution deployement). First of all, in this thesis, the previous work related to the troubleshooting of the wireless networks has been explored. It turns out that the fault detection and the diagnosis of wireless networks have been well studied in the scientific literature. Surprisingly, no significant references for the research work related to the automated healing of wireless networks have been reported. Thus, the aim of this thesis is to describe my research advances on ”Automated healing of LTE wireless networks using statistical learning”. We focus on the faults related to Radio Resource Management (RRM) parameters. This thesis explores the use of statistical learning for the automated healing process. In this context, the effectiveness of statistical learning for automated RRM has been investigated. This is achieved by modeling the functional relationships between the RRM parameters and Key Performance Indicators (KPIs). A generic automated RRM architecture has been proposed. This generic architecture has been used to study the application of statistical learning approach to auto-tuning and performance monitoring of the wireless networks. The use of statistical learning in the automated healing of wireless networks introduces two important difficulties: Firstly, the KPI measurements obtained from the network are noisy, hence this noise can partially mask the actual behaviour of KPIs. Secondly, these automated healing algorithms are iterative. After each iteration the network performance is typically evaluated te l-0 05 89 61 7, v er si on 1 29 A pr 2 01 1