Anomaly detection and diagnosis for automatic radio network verification

The concept known as Self-Organizing Networks (SON) has been developed for modern radio networks that deliver mobile broadband capabilities. In such highly complex and dynamic networks, changes to the configuration management (CM) parameters for network elements could have unintended effects on network performance and stability. To minimize unintended effects, the coordination of configuration changes before they are carried out and the verification of their effects in a timely manner are crucial. This paper focuses on the verification problem, proposing a novel framework that uses anomaly detection and diagnosis techniques that operate within a specified spatial scope. The aim is to detect any anomaly, which may indicate actual degradations due to any external or system-internal condition and also to characterize the state of the network and thereby determine whether the CM changes negatively impacted the network state. The results, generated using real cellular network data, suggest that the proposed verification framework automatically classifies the state of the network in the presence of CM changes, indicating the root cause for anomalous conditions.