# Framework for Behavioral Analysis of Mobile Networks
## 4 Experiments and Results
### Operation Phase  
In the operation phase, the trained classifier is used to find out the pattern that corresponds to new data collected from the network and to monitor possible changes in the pattern of a certain cell. Specifically, the tests carried out detected a total of nine pattern changes, of which 7 were sporadic cell behavior changes that return to their initial pattern after a short time period. However, the other two maintain the pattern change over time. Figures 6 and 7 show examples of some of the KPIs for a specific cell that experience a behavior change from Pattern 1 (red) to Pattern 4 (brown). Both figures show a shaded area with the colors associated with Patterns 1 and 4, which highlights the typical values of the cells belonging to these patterns for a particular KPI. In addition, a couple of horizontal lines are included to indicate the average value of each pattern. The dots represent the average value of the cell studied in each week. The analyzed cell changes its pattern in the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Accumulated (\%)** & **Average (\%)** \\ \hline Traffic and Throughput & 43.79 & 7.29 \\ Quality & 10.11 & 2.53 \\ Users & 11.55 & 2.88 \\ Use of resources & 23.3 & 5.82 \\ Others & 11.24 & 2.37 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Importances per category of KPI.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Key Performance Indicator** & **Importance (\%)** \\ \hline Cell throughput in downlink & 11.00\% \\ User throughput in downlink & 2.92\% \\ Volume data traffic in downlink & 11.73\% \\ Cell throughput in uplink & 7.35\% \\ User throughput in uplink & 2.11\% \\ Volume data traffic in uplink & 8.67\% \\ \hline QPSK usage rate in downlink & 2.34\% \\ QPSK usage rate in uplink & 3.51\% \\ Spectral efficiency downlink & 2.27\% \\ CQI & 2.00\% \\ \hline Average of connected users & 5.14\% \\ Maximum of connected users & 4.18\% \\ Restablishments success rate & 1.32\% \\ Restablishments per connected user & 0.91\% \\ \hline PRB usage rate in downlink & 3.16\% \\ PRB usage rate in uplink & 3.41\% \\ Blocking rate in signalling resources & 10.92\% \\ Signalling resources usage rate & 5.81\% \\ \hline Latency in downlink & 4.29\% \\ RSSI in uplink control channel & 1.80\% \\ RSSI in uplink shared channel & 2.24\% \\ UE distance & 1.22\% \\ SINR lower than 2 dB rate & 1.69\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Importances of each KPI in classification.  
third week, and it maintains the new pattern in the last two weeks of data. On one hand, Figure 6 shows a KPI (_Data Traffic in Downlink_) where the change of pattern can be clearly observed. On the other hand, Figure 7 reflects the similar behavior in some KPIs such as _CQI_, which can be observed in two different patterns, making the decision to detect a pattern change. Nevertheless, the pattern change can be observed since the values of the cell per week take values closer to the average and deviation values (shaded) of Pattern 4 in the first 3 weeks (brown), while the last ones are closer to the values of Pattern 1 (red).  
The tests were performed with the available dataset of a real LTE network, but the methodology followed for the implementation of the framework could be adapted with small changes to work with data from a 5G network. The pre-processing would undergo the main changes with the aim of adapting the data format of a 5G network to the format with which SOM has been implemented based on network performance indicator statistics. In the rest of the framework, no further significant changes would be required.