# Framework for Behavioral Analysis of Mobile Networks
## 4 Experiments and Results  
### Experiments Setup  
A battery of tests has been carried out to evaluate the performance of the proposed framework. Since it is difficult to find significant datasets from 5G networks, a pair of datasets of commercial LTE networks has been used. However, the proposed methodology is directly applicable to other technologies such as 5G. The largest LTE dataset is the one analyzed in more detail, and the results obtained with it are presented below. This dataset consists of 450 cells, where each one has 23 KPIs. These KPIs are related to different aspects of the cell such as throughput, traffic, quality or use of resources, among others. Table 2 shows the KPIs that have been included in the tests. Figure 4: Block diagram of the operation phase in the proposed framework. These KPIs are collected as a time series for a total of 46 days, with samples at hourly level. For the study carried out, weekends have been discarded, as well as holiday periods, resulting in a length of 25 days. From this time series, statistics are calculated to represent each KPI. In particular, the average, standard deviation, and 5 th and 95 th percentiles are calculated. If necessary, these statistics can be modified or completed it could be modified to include more metrics. Before SOM execution, statistics of each KPI are normalized by _Z-Score_ normalization. SOM algorithm is configured with the parameters of Table 3. The size of the map is adjusted to the 450 samples of the dataset, obtaining a total of 100 neurons in a 10 \(\times\)10 map. The initial neighborhood rate is set to 9, the maximum allowed by the size of the map. In addition, the learning rate is also set to a high value. A total of 100,000 iterations is established, which has been proven to be enough to get the algorithm to converge. The Batch training method is also established, which means that the samples are taken in the same order as in the dataset. The same decay function is maintained for both the learning and the neighborhood rate. Finally, other parameters of less relevance are kept at their default values. Post-processing is configured by the coefficients assigned to each metric. Table 4 shows how high, medium and low thresholds are set. \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Key Performance Indicator** \\ \hline \multirow{6}{*}{Traffic and Throughput} & Cell throughput in downlink \\  & User throughput in downlink \\  & Volume data traffic in downlink \\  & Cell throughput in uplink \\  & User throughput in uplink \\  & Volume data traffic in uplink \\ \hline \multirow{6}{*}{Quality} & QPSK usage rate in downlink \\  & QPSK usage rate in uplink \\  & Spectral efficiency downlink \\ \hline \multirow{6}{*}{Users} & Average of connected users \\  & Maximum of connected users \\  & Restablishments success rate \\  & Restablishments per connected user \\ \hline \multirow{6}{*}{Use of Resources} & PRB usage rate in downlink \\  & PRB usage rate in uplink \\  & Blocking rate in signaling resources \\  & Signaling resources usage rate \\ \hline \multirow{6}{*}{Others} & Latency in downlink \\  & RSSI in uplink control channel \\ \cline{1-1}  & RSSI in uplink shared channel \\ \cline{1-1}  & UE distance \\ \cline{1-1}  & SINR lower than 2 dB rate \\ \hline \hline \end{tabular}
\end{table} 
Table 2: KPIs by categories. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Size of map & \(10\times 10\) \\ Learning rate & 0.9 \\ neighbourhood rate & 9 \\ Iterations & 100,000 \\ Training method & _Batch_ \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SOM Configuration. After the execution of post-processing, a labeled dataset of 450 cells with 23 KPIs for each one and the type of each one is used. This dataset is divided for Cell Pattern Classification and Operation Phase. Specifically, 90% of the cells (405) are used to train Random Forest in the Cell Patterns Classification block and the remaining 10% (45) to test the effectiveness of the Operation Phase.