The intelligent detection of network coverage problems based on the clustering algorithm can automatically, quickly, and accurately locate network coverage problems by analyzing the running data of the existing network, ensuring continuous and high-quality communication services for users. Dynamic AP clustering and DBSCAN clustering algorithms are introduced to analyze MDT data, automatically locate coverage problems, and implement geographic aggregation, reducing labor costs and improving network efficiency.

Driven by the proliferation of data traffic and requirement of user experience improvement, mobile wireless network is evolving towards heterogeneous networks (HetNet). The telecom operator is experiencing unprecedented challenges on service maintenance and operational expenditure, which drives the demand for realizing automation in current heterogeneous networks. Cell outage detection is a functionality aiming to automatically detect and locate outages that occur in radio networks due to unexpected failures. Our work presents an automated cell outage detection mechanism in heterogeneous networks in which a clustering algorithm named Dynamic Affinity Propagation (DAP) is introduced. Minimization of drive test (MDT) measurements are collected and dimensionality reduced during regular operation and then fed into the clustering algorithm to find anomaly cells. The proposed mechanism has been implemented in the HetNet simulation environment, through which we have successfully detected the configured cell outages and located the specific outage areas.