# Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach
## 4 Evaluation
The rest is equally distributed between picocell interference and power degradation modeled failures [19], where the failure is generated by the central cells of the scenario 7, 8, 9, 10, 11, 12, and 13. Inference mechanisms (classifiers) should be able to distinguish between  
Figure 4: Evaluation scenario. Figure 5: Evolution in the number of received User Equipment (UE) reports (#UE reports) for different classification epochs (60 s). the different network statuses/failures as well as identifying the particular cell where they occur. For the validation of the approach, the samples that are associated to the 4004 loops are randomly divided in three groups. 40% of the loops are used by the automatic FE techniques to compute the list of selected indicators and/or the generation of synthetic ones. Another 40% are used in the training phase of the diagnosis functions, and the remaining 20% are used to test the performance of the diagnosis/classifier. Six of these diagnosis configurations are evaluatedSix of these diagnosis configurations are evaluated. The baseline, named \"Classic\", consists in performing the diagnosis based on all available classic (non-contextualized) indicators: the mean RSRP and the mean RSRQ per cell. This leads to a total of 24 indicators (two per each of the 12 picocells). In the second configuration, named \"Fusion NoFE\", the indicated classic metrics are joined by additional contextualized indicators. These are defined by separately considering the mean RSRQ and RSRP values for the samples gathered from the center, edge, influenced edge, influenced center, influencing edge and influencing center. This leads to a total of 278 indicators. Three configurations based on automatic FE mechanisms (applied over all the Fusion dataset) are tested. \"Fusion NCA\" makes use of the Neighborhood Component Analysis supervised technique for selection [30], whereas \"Fusion NCA + PCA\" adds a stage of unsupervised PCA extraction [31] after the selection. For both, 24 indicators are finally selected/generated to compare the results with those of Classic. Finally, \"Fusion NCA + PPCA\" performs the extraction phase by applying Probabilistic Principal Component Analysis (PPCA) unsupervised extraction [32], which is regarded as an improvement over PCA [32]. Having 24 indicators coming from NCA as inputs, it generates 15 synthetic ones. Alternatively, the impact of reducing the dimensionality of the Fusion set without using automatic techniques is assessed via the \"Fusion Manual\" approach. In this, a troubleshooting expert selects a total of 24 metrics from the set of all the available indicators (both classical and contextualized). This expert follows the criteria where the chosen metrics are those that are considered most likely to clearly serve to identify each class. Particularly, those selected are the mean RSRP for the centers of the cell (considered to be useful to detect the power degradation failures) and the RSRQ of the edges (assumed to be suitable for the detection of interference cases). The confusion matrices for Classic and Fusion NoFE configurations are represented in Figure 6 in order to provide an initial understanding on the diagnosis process. This shows the set of cases identified as the correct ones or any other network status. Here, each status is labelled as 1: normal, 3: macrocell interference, 2<cell_number>: picocell interference by <cell_number> (e.g., 207 for interference by cell 7) and 3<cell_number>: picocell power degradation in <cell_number> (e.g., 411 for power degradation in cell 11). Therefore, Figure 6 shows the enhancement in the classification behaviour by using one or another configuration. Here, it is seen how the number of samples that are not in the main diagonal decreases with the fact of using Fusion NoFE configuration. This is translated to a better performance by the classifier.