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tems™ visuAlizAtioN 7.1 eNterPrise
Optimization Decision Support
Intra-frequency Neighbor Optimization
makes it possible to easily
verify the neighbor plan and find both missing neighbors and non-utilized
existing neighbors in a WCDMA network. In WCDMA, an accurate neighbor
plan is crucial for network performance. Missing neighbor statistics, as re-
ported by actual users, existing neighbor usage, and pilot pollution statistics
are presented. In TEMS Visualization 7.1 Enterprise, new per-IMSI statistics
allow classification of missing neighbors as UE or network issues. This in-
formation is linked to the map view for easy analysis. Changes can be made
and saved to a BulkCM format file for import into the OSS-RC.
Coverage Area Optimization
allows overshooting cells to be quickly
identified and down-tilts adjusted. An algorithm calculates an overshooting
distance for each cell. During processing, statistics are calculated on the
number of overshooting calls and the number of calls established in poor
quality. Detailed investigation of the calls established in each distance band
and the quality of those calls can also be performed in charts and on the
map.
Detailed Investigations
Exception Analysis
summarizes all the GPEH messages recorded for
the selected scope and all the TEMS Visualization events generated. For
selected GPEH messages, it is possible to drill down even further and get
counts of the occurrences for different RAB types and also different cause
values. This is an extremely powerful way to determine the root causes of
network problems. Calls containing the selected events can then be sent to
Call Analysis.
Call Analysis
allows drilldown to the individual call level. Calls high-
lighted in any feature can be sent to Call Analysis for in-depth examination.
The sequence of signaling messages can be seen, and the reasons behind
problems such as blocked and dropped calls can be investigated in great
detail. Users can follow radio measurements per call, view detailed content
for individual messages, and retrieve distance and quality information.
Large groups of calls can also be analyzed for patterns. This is an extreme-
ly powerful feature which can, for example, quickly determine if all dropped
calls in a cell are generated by the same user or on the same RAB type.
