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 heterogeneity of these networks has significantly increased their operational cost. Typical faults in these networks may be related to equipment breakdown and inappropriate planning and configuration. In this context, automated troubleshooting and self optimization have been introduced as part of Self Organizing Networks (SON) standard of LTE. SON aims at reducing the operational cost and providing high-quality services for the end-users. This can reduce service breakdown time for the clients, resulting in the decrease in client switchover to competing network operators. This book explores the use of statistical learning for the automated healing and optimization process. In this context, the effectiveness of statistical learning for automated Radio Resource Management (RRM) has been investigated. The automated healing optimization methodology has been applied to 3G Long Term Evolution (LTE) use cases for healing the mobility and interference mitigation parameters.

Autorentext

Moazzam Islam Tiwana received his M.E. degree in Digital Telecommunication Systems from ENST, Paris, France, in 2007 and a Ph.D. degree in Mobile Communications from Telecom SudParis, Paris, France, in 2010. His PHD work was in the R&D department of Orange Labs. He has more than seven years of industrial and academic experience.