Big Data Analytics for Automated QoE Management in Mobile Networks

Abstract

Over the last years, there has been a significant increase in the number of services in mobile networks. This trend has forced operators to change their network management processes to ensure adequate user QoE, instead of adequate QoS. As a result, customer experience management is now a critical task for mobile network operators, who demand tools for QoE monitoring on an individual user basis. With the latest advances in information technologies, the newest TMA solutions can leverage the huge amount of information available from network elements and interfaces in mobile networks. However, data processing algorithms in these tools are still to be defined. In this work, we review the shortcomings and challenges in the use of TMA applications in mobile networks, and how these can be empowered by big data analytics. For this purpose, a methodology to validate a generic big-data-driven TMA framework with user terminal agents in a real cellular network is outlined. A use case is presented to show the potential and limitations of these applications for monitoring end-user QoE in a live LTE network.