## 4 Solution Methodology
### Baseline Algorithm 1--Network Metrics Clustering
### Network Metric Clustering Analysis

The network metric clustering aims at the separation of the measurement data into groups with similar behavior based on the network metrics included (RSRP, active RSRP, SINR, downlink throughput). The clustering effectiveness can be seen in the projection of the multi-dimensional dataset in a projected plane such as the PCA projection. In the 2-dimensional component space of PCA, we used coloring to define each cluster detected, expecting clear distinction between the clusters [Figure 4]. We are also generating centroid classes to profile each cluster with a label that can be used to detect measurement groups of high or low qualities such as the ones shown in Table 4. We could see that there were 10 generated clusters with meaningful partitioning that uses a combination of SINR and RSRP cluster labels to provide clear network performance information. For instance, Cluster 0 is described as \"Medium SINR, Medium RSRP\" and Cluster 1 as \"High SINR, Very Low RSRP\", etc., as seen in the overview of the cluster qualitative results. Conditional probability analysis was used in the centroids to identify serving cells that are more related to the clusters with the deteriorated coverage and quality. Provided the list of problematic cells, we used their neighboring cells to improve the radio quality of the cell sector's area, therefore producing azimuth steering computations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Color** & **Cluster Index** & **SINR Profile** & **RSRP Profile** \\ \hline  & 0 & Medium & Medium \\  & 1 & High & Very Low \\  & 2 & High & High \\  & 3 & Low & Very Low \\  & 4 & Very Low & Medium \\  & 5 & Very High & Very High \\  & 6 & Low & Low \\  & 7 & Very Low & Very Low \\  & 8 & Low & High \\  & 9 & High & High \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Clustering result legend including color, index, and SINR/RSRP profile quality class. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Field** & **Description** & **Example Values** \\ \hline Mobile ID & An identifier for each mobile terminal included in the measurements & Mob\_1,2... N \\ Timestamp & Time identifier of the measurement & 1638035033 \\ Serving Cell & An identifier of the cell that the mobile terminal is connected to & Cell 2,1, Cell 3,2 \\ RSRP & Received signal strength from serving gNB (idle) & \(-\)72 dBm \\ Active RSRP & Received signal strength from serving gNB (transmission) & \(-\)72 dBm \\ SINR & Signal-to-Interference-and-Noise Ratio (transmission) & 9 dB \\ Downlink & The data rate achieved by the mobile terminal during download & 34.2 Mbps \\ Throughput & The latitude position of the mobile terminal & 25.61 (deg) \\ Longitude & The longitude position of the mobile terminal & 37.61 (deg) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mobile Terminal simulation measurement dataset analysis. ### Spatial Clustering Analysis

The spatial clustering algorithm is based on the automated detection of population density hot-zones. It leverages the localization information acquired from the dataset by transforming it into population density centroids that can be used to compute azimuth steering, which will increase the number of mobile terminals that are close to the center of the sector antenna. As seen in Figure 5, the algorithm correctly identified densities that were correlated with the predefined zones of the mobility model parametrization area. This hints that the performance of the generated azimuth steering actions is an improvement with respect to the symmetrical deployment. However, the resulting population density areas did not differentiate on the traffic demand of the mobile terminals. We expect that this will result in azimuth steering actions toward low traffic zones that will be considered as incorrect. Figure 4: Network metric clustering results (PCA) colored by cluster.