No abstract

No abstract

No abstract

A data-mining framework for analyzing a cellular network drive testing database is described in this paper. The presented method is designed to detect sleeping base stations, network outage, and change of the dominance areas in a cognitive and self-organizing manner. The essence of the method is to find similarities between periodical network measurements and previously known outage data. For this purpose, diffusion maps dimensionality reduction and nearest neighbor data classification methods are utilized. The method is cognitive because it requires training data for the outage detection. In addition, the method is autonomous because it uses minimization of drive testing (MDT) functionality to gather the training and testing data. Motivation of classifying MDT measurement reports to periodical, handover, and outage categories is to detect areas where periodical reports start to become similar to the outage samples. Moreover, these areas are associated with estimated dominance areas to detected sleeping base stations. In the studied verification case, measurement classification results in an increase of the amount of samples which can be used for detection of performance degradations, and consequently, makes the outage detection faster and more reliable.

This article presents an automatic malfunction detection framework based on data mining approach to analysis of network event sequences. The considered environment is Long Term Evolution (LTE) for Universal Mobile Telecommunications System (UMTS) with sleeping cell caused by random access channel failure. Sleeping cell problem means unavailability of network service without triggered alarm. The proposed detection framework uses N-gram analysis for identification of abnormal behavior in sequences of network events. These events are collected with Minimization of Drive Tests (MDT) functionality standardized in LTE. Further processing applies dimensionality reduction, anomaly detection with K-Nearest Neighbors (K-NN), cross-validation, post-processing techniques and efficiency evaluation. Different anomaly detection approaches proposed in this paper are compared against each other with both classic data mining metrics, such as F-score and Receiver Operating Characteristic (ROC) curves, and a newly proposed heuristic approach. Achieved results demonstrate that the suggested method can be used in modern performance monitoring systems for reliable, timely and automatic detection of random access channel sleeping cells.