Automated optimization of key WCDMA parameters: Research Articles
Wireless Communications & Mobile Computing
Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)
Busy bursts for trading off throughput and fairness in cellular OFDMA-TDD
EURASIP Journal on Wireless Communications and Networking - Special issue on fairness in radio resource management for wireless networks
Statistical learning for automated RRM: application to eUTRAN mobility
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Practical Applications of Data Mining
Practical Applications of Data Mining
Autonomic downlink inter-cell interference coordination in LTE self-organizing networks
Proceedings of the 7th International Conference on Network and Services Management
| Hi-index | 0.00 |
With the evolution of broadband mobile networks towards LTE and beyond, the support for the internet and internet based services is growing. However, the size and operational costs of mobile networks are also growing. Self Organizing Networks (SON) are introduced as a part of the specifications of the LTE standard with the purpose of reducing the Operation and Maintenance costs of the mobile networks. This paper introduces a novel framework for automated Radio Resource Management (RRM) in LTE SON. This framework deals with the self-optimization and self-healing features of SON. The data mining technique of linear regression has been used to derive the functional relationship, known as model, between Key Performance Indicators and RRM parameters. The proposed framework uses this model in two ways: first, for network monitoring, which is the first step of the self-healing procedure and secondly, to devise a handover auto-tuning algorithm as part of the self-optimization procedure. The detailed results obtained for the finished case studies, demonstrate the effectiveness and usefulness of this approach.