%0 Journal Article
%T An Approach for Network Outage Detection from Drive-Testing Databases
%A Jussi Turkka
%A Fedor Chernogorov
%A Kimmo Brigatti
%A Tapani Ristaniemi
%A Jukka Lempi£żinen
%J Journal of Computer Networks and Communications
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/163184
%X 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. 1. Introduction Modern radio access networks (RAN) are complex infrastructures consisting of several overlaying and cooperating networks such as next-generation high-speed-packet-access (HSPA) and long-term evolution (LTE) networks and as such are prone to the impacts of uncertainty on system management and stability. Classical network management is based on a design principle which requires knowledge of the state of all existing entities within the network at all times. This approach has been successfully applied to networks of limited scale but it is foreseen to be insufficient in the management of future complex networks. In order to maintain a massive multivendor and multi-RAN infrastructure in a cost-efficient manner, operators have to employ automated solutions to optimize the most difficult and time-consuming network operation procedures. Self-organizing network concept [1] has emerged in the last years, with the goal to foster automation and to reduce human involvement in management tasks. It implies autonomous configuration, optimization, and healing actions which would result in a reduced operational burden and improve the experienced end user quality-of-service (QoS). One of the downsides of the SON concept is the necessity to gather larger amounts of operational data from user equipment (UE)
%U http://www.hindawi.com/journals/jcnc/2012/163184/