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Mobility Robustness Optimisation (MRO). ○. Mobility Load Balancing optimisation (MLB). ▫. Q4, additional use cases to TR ○. Support for SON based automatic RACH optimization is introduced in 3GPP Release 9 specifications TS. and TS and is discussed in TR [1] 3GPP “E-UTRA Radio Resource Control (RRC) Protocol specification ( Release [2] 3GPP TR V, “Evolved Universal Terrestrial Radio Access.

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The method is cognitive because it requires training data for the outage detection. This approach has been successfully applied to networks of limited scale but it 36.9002 foreseen to be insufficient in the management of future complex networks.

3GPP TR (1 of 3) – E-UTRAN: Self-configuring and SON use cases and solutions

The presented method is designed to detect sleeping base 3hpp, network outage, and change of the dominance areas in a cognitive and self-organizing manner. Classical network management is based on a design principle which requires knowledge of the state of all existing entities within the network at 36.9002 times. The essence of the method is to find similarities between periodical network measurements and previously known outage data. 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.

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An Approach for Network Outage Detection from Drive-Testing Databases

Moreover, 3gp areas are associated with estimated dominance areas to detected sleeping base stations. 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.

In order to maintain a massive multivendor and multi-RAN 3tpp in a cost-efficient manner, operators have to employ automated solutions to optimize the most difficult and time-consuming network operation procedures. 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.

One of the downsides of the SON concept is the necessity to gather larger amounts of operational data from user equipment UE.

In addition, the method is autonomous because it uses minimization of drive testing MDT functionality to gather the training and testing data. 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. 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.

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For this purpose, diffusion maps dimensionality reduction and nearest neighbor data classification methods are utilized. A data-mining framework for analyzing a cellular network drive testing database is described in this paper.