## 4 Assessment
### KQI and Radio Logs Correlations  
Once the impacts of the development of a social event (in this case a football match) on the QoE and radio behavior patterns of the environment have been observed, the next step is to find out which physical parameters have been relevant for said influence.  
First of all, a correlation matrix between the parameters obtained in the MODEM log and the KQI corresponding to the NetTest [42] is captured in Figure 12. This matrix shows the correlations between pairs of parameters in a color code that varies in intensity from red (negative correlation with value \(-1\)) to dark blue (positive correlation with value \(1\)).  
By looking at the radio parameters obtained from the MODEM log, the correlations of these parameters present different fluctuations depending on the operator configuration. This fact is exemplified in 371 Op, a case where it can be deduced that there is a negative  
Figure 11: Mobile insight trace packet time granularity.  
Figure 12: MODEM log parameter–KQI correlations for NetTest.  
correlation between cell identification and upload time, while in the rest of the cases, a higher RSRP value means a better throughput (direct correlation). Based on the results, a higher correlation with CID is shown in node 371 operator B. This fact likely means that the node changes the serving cell with the performance varying depending on its RSRP and RSRQ. Concerning operator A, the RSRP and RSRQ show a relatively appreciable correlation factor for the UL/DL throughput (above 30%). Additionally, noteworthy, although somewhat expected, is the high correlation of the RSSI with the KQI, even higher than that obtained with RSRP, since it is not an averaged value that better reflects the received signal. In this sense, when the selected band has a negative influence on the received signal RSSI (as in node 371, operator A), there is a negative impact on the download throughput (\(-0.73\)). For the case of the band for operator B, both node 361 (\(-0.096\)) and node 371 (\(-0.19\)) present a relatively smoothed influence, even though the latter had its operator A interface in the same device.  
Remarkably, both throughputs are closely correlated, especially in operator B (nodes 361 and 371). This fact is related to the effect of the load on the mobile network, which means that when there are many users connected, both upstream and downstream resources are limited at the same time. In other words, when the cell is unloaded, there is a greater probability of finding available resources in both directions.  
A further step in exploring the factors most involved in the degradation of NetTest performance indicators is the study of the correlations with some of the physical layer parameters obtained from the Mobile Insight traces. These parameters are the MCS and TBS in layers 0 and 1.  
As shown in the Figure 13, the correlations between KQI and such parameters are not very strong given the low intensity of the correlations (graphically represented with softer colors). This is possibly because there is a more significant interaction with the radio signal conditions that depend on weather directly, and not so much on the performance of the service that is the result of long-term averaging.  
In any case, the analysis provided by matrices shown in Figure 12 together with Figure 13 allows identifying patterns of influence between KQIs, and between KQIs and the radio parameters that may be negatively affecting them. That is the case for operator B, where it can be deduced that the change of cell may be the cause of a degraded performance.