3GPP TR 36.902 PDF

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

Moreover, these areas are associated with estimated dominance areas to detected 3glp base stations.

An Approach for Network Outage Detection from Drive-Testing Databases

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. 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.


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.

Self-organizing network concept [1] has emerged rt the last years, with the goal to 3gpp automation and to reduce human involvement in management tasks. 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. It implies autonomous configuration, optimization, and healing actions which would 3gop in a reduced operational burden and improve the experienced end user quality-of-service QoS.

The essence of the method is to find similarities between periodical network measurements and previously known outage data.

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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 approach has been successfully applied to networks of limited scale but it is foreseen to be insufficient in the management of future complex networks. The method is cognitive because it requires training data for the outage detection. One of the downsides of the 3.902 concept is the necessity to gather larger amounts of operational data from user equipment UE.

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.