Detecting cellular network anomalies using the knowledge discovery process

Abstract

Analytical companies unanimously forecast the exponential growth of mobile traffic consumption over the next five years. The densification of a network structure with small cells is regarded as a key solution to meet growing capacity demands. The manual management of a multi-layer network is a very expensive, error prone, and sluggish process. Hence, the automation of the whole life cycle of network operation is highly anticipated. To this aim 3GPP introduces a self-management concept referred to as SON. It is envisioned that SON updates information concerning the latest network conditions through the MDT mecha- nism. MDT enables a network operator to collect radio and service quality measurements from regular mobile phones. Self-healing is SON’s functionality that implements fault management in radio networks. The automated and timely detection of a malfunctioning cell is one of the crucial challenges for network operators. The thesis investigates the topic of self-organizing radio networks and proposes a cell outage detection framework based on MDT measurements and advanced data mining techniques. The sequential analysis of LTE network events underlies the proposed idea. The conducted research demonstrates the feasibility of the original idea and designs the KDD process for the automated analysis of cell failures. The second part of the study improves the computational complexity and performance of the proposed solution. Besides, the research discovers the impact of location accuracy and scarcity of MDT measurements on the quality of cell outage detection. The validation of the framework has been conducted on the state-of-the-art LTE/LTE-A system level simulator. Results demonstrate reliable and timely detection of a malfunctioning cell. Therefore, the developed cell outage detection solution can be considered for the practical validation and implementation.