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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Rr 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.

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

For this purpose, diffusion maps dimensionality reduction and nearest neighbor data classification methods are utilized. 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.

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In addition, the method is autonomous because it uses minimization of drive testing MDT functionality to gather the training and 3glp data. A data-mining framework for analyzing a cellular network drive testing database is described in this paper. 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.

Self-organizing network concept [1] 3pp emerged in the last years, with the goal to foster automation and to reduce human involvement in management tasks. One of the downsides of the SON concept is the necessity to 3.902 larger amounts 3.902 operational data from user equipment UE.

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

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The method is cognitive because it requires training data for the outage detection. The essence of the method is to find similarities between periodical tf measurements and previously known outage 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. Te 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. This approach has been successfully applied to networks of limited scale but it is foreseen to be insufficient in 3hpp management of future complex networks.

Classical network management is based on a design principle which requires knowledge of hr state of all existing entities within the network at all times.