Hierarchical analysis of GSM network performance data

Anybody have an idea of GSM network KPI, like CSSR, drop call, HOSR values.
What should be the recommended threshold for any GSM network?

gsm kpis

Hierarchical analysis of GSM network
performance data
Mikko Multanen, Kimmo Raivio and Pasi Lehtim¨aki
Helsinki University of Technology
Laboratory of Computer and Information Science
P.O. Box 5400, FI-02015 HUT - Finland
Abstract. In this study, a method for hierarchical examination and
visualization of GSM data using the Self-Organizing Map (SOM) is described.
The data is examined in few phases. At first temporally averaged
data is used and then, in each phase some of the data is discarded and the
rest is examined in more detail. The SOM is used both in clustering and
in visualization. The actual clustering is performed to the nodes of the
SOM to lower the computational cost and to help to understand better
the properties of the clusters.
1 Introduction
The purpose of this project was to develop a method to explore the data of an
entire GSM network. This method is an extension to the methods developed by
the group for the analysis of mobile radio access networks [1] [2] in which only
small geographical areas of the network were used. The main problem in the
current project was on the one hand to reduce the amount of data so that the
results would not be overwhelming to go through and on the other hand to give
an extensive picture of the performance data.
To achieve this goal a hierarchical method was developed where the sensi-
tivity of the examination is increased gradually. In every phase, a proportion of
the data samples are discarded so that the rest can be examined in more detail.
The data samples are clustered at each phase and one cluster is chosen to the
next phase. The whole process builds a tree-like structure where similar data
samples are in the same branch of the tree.
The main tool used in the project was the Self-Organizing Map (SOM) [3].
The SOM was used because of its data reduction abilities and mainly because it
is highly visual. The SOM is a good method when high dimensional data has to
be represented. It maps multi-dimensional data to a two-dimensional grid which
can easily be visualized. The visualization of the data was one of the main goals
of this project. Also a method for ordering the clusters was developed so that
the interestingness between the clusters can be compared more easily.
2 Network and data
A simplified description of the architecture of a GSM (Global System for Mobile
communications) network is shown in Fig. 1. The subscriber carries the Mobile
Station (MS). The Base Transceiver Station (BTS) contains radio transceivers
Fig. 1: The architecture of a GSM network.
and handles the radio-link protocols. The Base Station Controller (BSC) man-
ages the radio resources for one or more BTSs. BTSs and BSCs together control
the Air interface to the MS and they communicate across the Abis interface.
The network is connected to other telephone networks through the Mobile ser-
vice Switching Center (MSC). The BSC and the MSC communicate across the
A interface [4].
The data had been collected from a real operative network. It was stored as
counters of different events in the network elements. The counters were stored
every hour and then reseted for the next cycle. There were thousands of counters
in the database and only about hundred of them were used in this project.
A Key Performance Indicator (KPI) is calculated from one or more counters.
The KPIs were defined by the network manufacturer. The KPIs were used
because they are much more intuitive than the plain counters. It is much easier
to understand what one KPI represents than what the plain counters from which
the KPI is calculated represent. On the other hand some information is also lost
with KPIs because one KPI value contains the information of many counters.
The most critical KPIs were TCH (Traffic CHannel) Drop Rate, which indi-
cates abnormal service interruptions, and TCH Blocking, which indicates service
denials. These KPIs have a direct effect to the quality experienced by the sub-
scribers [5].
3 Methodology
3.1 Two-level clustering
The Self-Organizing Map (SOM) [3] consists of a regular low dimensional grid,
in this case two dimensional. The units of the grid are represented by prototype
vectors whose dimension is the same as the dimension of the data vectors. The
prototype vectors form an elastic net. When the SOM is trained the net settles so
that the prototype vectors represent the training data as well as possible without
losing the net structure. Because of the net structure it is straightforward to
visualize the SOM in two dimensions.
The clustering of the data vectors is done in two stages [6]. At first a SOM
is trained with normalized data. The prototype vectors also known as the nodes
of the SOM can be seen as the clusters of the first level. Then the nodes are
clustered. This is the second level of the clustering. The number of the prototype
vectors is much smaller than the number of the data points. So by using the
SOM the data for the actual clustering is reduced considerably.
The clustering of the SOM is made with a hierarchical agglomerative cluster-
ing algorithm. At first each node is assigned to its own cluster. Then distances
between all the clusters are calculated and two nearest ones are merged. The
distance d between two clusters Ck and Cl is calculated by using the average
linkage:
d(Ck,Cl) = Pi,j kxi - xjk
NkNl
, (1)
where xi . Ck, xj . Cl, k 6= l and Nk is the number of data points in the
cluster Ck. The merging of the clusters is continued until there is only one
cluster left which contains all the nodes. These operations build a clustering
tree (dendrogram). Several different clusterings of the data can be made by
cutting the tree from different levels. In this project, the number of the clusters
was fixed to the square-root of the number of the data vectors used in training
of the SOM.
This two-level approach decreases the computational cost of the clustering
compared to the clustering of the data outright. If there were N data vectors
which should be clustered using this two-level clustering only M nodes of the
SOM have to be clustered where M is much smaller than N. However the data
can be clustered only to M clusters at the most and also the SOM must be
trained. The overall computational cost is smaller with a two-level clustering
than with a straightforward clustering of the data samples.
3.2 Interest values
The clusters of the SOM can be compared by calculating interest values for
them. At first interest values are calculated for the individual data vectors. The
interest value of one cluster is got by taking the average of the interest values of
the data vectors which belong to the cluster. KPI values are mapped nonlinearly
to interest values. Expert knowledge is used in the construction of the mapping.
A simple sigmoid function can be used. The interest value is near one when the
KPI gets interesting values and otherwise near zero.
The following sigmoid function is used in the calculation of the interest value
of a cluster for a KPI:
interesti,k = X
x2Ci
scalek
1 + e-steepnessk(xk-thresholdk)
w(xk), (2)
where x is a data vector, i the index of the cluster and k the index of the KPI.
Ci is the set of the data vectors of the cluster i. w(xk) is a traffic weight value
which is calculated separately for SDCCH, TCH and HO related KPIs. The
more traffic there is the more interesting it is. The weight value is calculated
using the following formula:
w(xk) = a
tk
mtk
+ (1 - a) , (3)
Fig. 2: A simple view of the system.
where tk is the traffic related to the KPI k, mtk is the mean of the traffic
values in the data set and a is a parameter which defines how much traffic has
influence. The relative influence of different KPIs to the interest value is defined
by scale parameter. The accepted operational area of a KPI is defined with
steepness and threshold parameters. For example, threshold can be set to
the border of the unacceptable behavior. If steepness is positive values bigger
than threshold are unacceptable and if steepness is negative values smaller
than threshold are unacceptable. Expert knowledge was used in setting these
parameters. The overall interest value of a cluster is calculated by summing the
interest values of the cluster over all the KPIs.
4 Data analysis
4.1 First and second phase
The following procedure is used in finding the most interesting parts from the
data of the entire network. In the first phase the feature vector contained the
averages of the selected KPIs over the whole two month measurement time for
every BTS. A SOM was trained with this data and the nodes of the SOM were
clustered as described above in Section 3.1. After the clustering interest values
were calculated for the clusters using the method described in Section 3.2. One
cluster was selected and the BTSs which hit the selected cluster were chosen to
the next phase.
In the second phase, the feature vector contained the averages of the KPIs
over one day for every BTSs which were selected in the first phase. Different
days were now separate feature vectors. A new SOM was trained and clustered.
Interest values were calculated for the clusters and one cluster was selected and
the days and BTSs which hit the cluster were chosen to the next phase. Also
one KPI of the used KPIs was chosen so that in the counter phase there was
only one KPI and the most interesting days and BTSs to examine.
An overview of the system is shown in Fig. 2. In the first and the second
phase, the SOM is colored with the help of the interest values of the clusters so
that it is easier to see which parts of the map are more interesting. The number
of data vectors is kept roughly the same in the phases because the number of
temporally averaged data vectors is decreased by selecting only one cluster and
before the SOM of the next phase the temporal precision of the residual data is
10 clusters
10
10
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4
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5
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2
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7
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1
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1
1
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1
1
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1
1
1
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1
10
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1
1
1
1
1
0 0.5 1
tch_radio_fail
tch_rf_old_ho
tch_a_if_fail_call
tch_a_if_fail_old
tch_norm_seiz
tch_drop
Cluster 10 hits 1470
0 0.5 1
tch_radio_fail
tch_rf_old_ho
tch_a_if_fail_call
tch_a_if_fail_old
tch_norm_seiz
tch_drop
Cluster 6 hits 23
0 0.5 1
tch_radio_fail
tch_rf_old_ho
tch_a_if_fail_call
tch_a_if_fail_old
tch_norm_seiz
tch_drop
Cluster 8 hits 145
0 0.5 1
tch_radio_fail
tch_rf_old_ho
tch_a_if_fail_call
tch_a_if_fail_old
tch_norm_seiz
tch_drop
Cluster 4 hits 81
0 0.5 1
tch_radio_fail
tch_rf_old_ho
tch_a_if_fail_call
tch_a_if_fail_old
tch_norm_seiz
tch_drop
Cluster 5 hits 474
0 0.5 1
tch_radio_fail
tch_rf_old_ho
tch_a_if_fail_call
tch_a_if_fail_old
tch_norm_seiz
tch_drop
Cluster 7 hits 295
0 0.5 1
tch_radio_fail
tch_rf_old_ho
tch_a_if_fail_call
tch_a_if_fail_old
tch_norm_seiz
tch_drop
Cluster 2 hits 723
0 0.5 1
tch_radio_fail
tch_rf_old_ho
tch_a_if_fail_call
tch_a_if_fail_old
tch_norm_seiz
tch_drop
Cluster 9 hits 181
0 0.5 1
tch_radio_fail
tch_rf_old_ho
tch_a_if_fail_call
tch_a_if_fail_old
tch_norm_seiz
tch_drop
Cluster 3 hits 1339
0 0.5 1
tch_radio_fail
tch_rf_old_ho
tch_a_if_fail_call
tch_a_if_fail_old
tch_norm_seiz
tch_drop
Cluster 1 hits 410
Fig. 3: On the left side is the clustering of the TCH Drop Rate SOM of the
counter phase and on the right side are the normalized mean counter values
of the clusters. These can be used in the comparison between the clusters.
Diagrams are sorted according to the value of the TCH Drop Rate KPI.
increased. There could have been more phases, if for example the measurement
time had been longer, but to this data these two were sufficient.
4.2 Counter phase
Now the feature vector was created from the counters of the chosen KPI of the
second phase cluster. The hours of the chosen days for the selected BTSs were
investigated as separate vectors. A SOM was trained and clustered. The value of
the KPI depends on different counters so the clusters are distinguished by the fact
that different counters might be dominant in different clusters. After this phase
the most interesting data can be chosen to a more case-specific examination.
The clustering of the counter phase SOM and the bar graphs of the normal-
ized means of the counters in the clusters are shown in Fig. 3. TCH Drop Rate
KPI was chosen to the counter phase because it got the highest interest value in
the second phase. In the first and the second phase, the most interesting clusters
were chosen. This explains why the TCH Drop Rate KPI gets high values all
over the SOM, except in the clusters 10 and 6, in the counter phase. Although
the TCH Drop Rate is near constant the values of the counters vary. In this
case the counters tch norm seiz, tch a if fail old, tch a if fail call,
tch rf old ho and tch radio fail had the biggest increasing effect to the av-
erage value of the TCH Drop Rate KPI on the map. Counters whose influence
was much smaller than these were dropped out from the visualization.
5 Conclusions
In this project, a hierarchical method to examine the data of an entire mobile
network has been presented. The data is examined hierarchically in steps. KPI
data is used in the first phases because it is much more intuitive than raw
counter data. On every phase, data is clustered and one cluster is selected.
Interest values are calculated for the clusters to help the selection. The amount
of the data is reduced in every phase so that it can be examined in more detail in
the next phase. This method creates a tree like structure where the data vectors
of one branch are similar with each other. It gives a quick representation of the
data of the network.
The developed method takes advantage of the expert knowledge of the net-
work. The KPI formulas are defined by experts and the parameters of the
interest function must be defined before the method can be used. Also the KPIs
which will be used in the method have to be chosen. However, the method gives
some feedback of the chosen KPIs, how interesting they are according to the
interest function.
Hierarchical examination proved to be a workable method. It reduces effec-
tively the amount of data which have to be examined manually. In this project,
the rough estimate of the data was created by taking average which might hide
some of the interesting parts of the data. So, a more intelligent method should
be developed. Perhaps some kind of weighted average which uses the interest
function would be a better one. Also the KPI interest calculation function could
be replaced with a more advanced one. Although GSM data was used in the
project the method described in this paper can be used as well for the analysis
of 3G radio access networks.
References
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