A Model of Telecommunication Network Performance Anomaly Detection Based on Service Features Clustering

It proves to be increasingly promising to evaluate the network coverage and network capacity via applying data generated by network interaction provided by the mobile operators in recent years. Self-organized optimization of network coverage and network capacity is a key solution to cope with the rapid growth of mobile data services and user expectation. In this paper, a network performance anomaly detection model based on service feature clustering is proposed. First, the complexity of the wireless network environment and the difference of user behavior are fully considered, an ensemble clustering algorithm is adopted for scene classification by combining the features of various data services with multi-dimensional physical scene characteristics, and, then, the cell categories with different characteristics are marked. Second, each category matches the corresponding network indicators and the weights of each indicators, which are trained from historical data. Finally, network performance anomaly detection is conducted on the basis of these indicators in every scene so that a new approach to evaluate the performance of wireless network can be realized. We apply this method to our long term evolution network, and the actual operation results have confirmed that the algorithm in question is highly effective and relevant.