A dynamic affinity propagation clustering algorithm for cell outage detection in self-healing networks

With the rapid development of the mobile wireless system, the operator is experiencing unprecedented challenges on service maintenance and operational expenditure, which drives the demand for realizing automation in current networks. The cell outage detection is considered as an effective way to automatically detect network fault. Our work presents an automated cell outage detection mechanism in which a clustering technique called Dynamic Affinity Propagation (DAP) clustering algorithm is introduced. Performance metrics are collected from the network during its regular operation and then fed into the algorithm to produce optimal clusters for further anomaly detection. The proposed mechanism has been implemented in the LTE-Advanced simulation environment, through which we have successfully detected the configured cell outages and located their specific outage areas.