## 4 Solution Methodology
### Baseline Algorithm 1--Network Metrics Clustering
By identifying population density centers, we can then make decisions as to what azimuth steering action we can take to move the azimuth angle of the antennas toward these centers, maximizing the mobile terminal's RSRP. This approach can be considered as an improvement vs. the symmetrical, hexacomb-based initial scenario and the rest of the literature, however, it does not come without its own drawbacks. Population density and traffic hot-zones cannot be differentiated by this method due to the lack of network metric information in the clustering process. This results in all density centers being included in the azimuth steering process, which can result in maximizing coverage for zones that are of very low traffic, making minimal impact in the improvement of the network operation. For the spatial scenario, UEs are clustered only on their spatial coordinates. Again, each gNB is associated to a subset of the clusters and is rotated to center its direction to the geometric center of these clusters. The steps of the spatial clustering algorithm can be shown in Baseline Algorithm 2. ```
0: Azimuth Steering Action for each gNB Input: 1. Serving gNB for each UE
2. Cluster label for each UE
3. RSRP measurement for each UE
4. SINR measurement for each UE Begin
5:Foreach Cell in Cellsdo: 1. Find the Subset of Clusters that are served by Cell
2. Calculate the geometric center of the clusters centroids in that subset
3. Calculate the delta azimuth between the Cell direction and the geometric center
4. Rotate Cell to make the delta azimuth zero
5:End
6:End
```

**Algorithm 2**Azimuth Steering based on Spatial Clustering

### The Proposed Scheme: Hybrid Network--Spatial Metric Clustering

The proposed scheme is an approach that aims at exploiting the techniques of both approaches above-mentioned (Baseline 1 and 2). It is using the spatial clusters as a method of detecting the exact location of the high population density zones that is key to the correct computation of azimuth steering actions while using the network metric cluster traceback analysis to distinguish between zones with high traffic demand and idle user zones. This essentially differentiates between clusters that are traffic hot-zones and population hot-zones, which is the key improvement vs. the Baseline 2 case. The accuracy of the azimuthsteering, achieved by the inclusion of the location information, is also expected to make the proposed scheme outperform the Baseline 1 case, which generalizes the location of the traffic hot-zone within the cell's coverage area. Due to the nature of the data distributions of the spatial and non-spatial, the literature indicates that hierarchical DBSCAN (HDBSCAN) [26] is the most suitable clustering approach to accurately segment the data, since it is non-parametric, easy to configure, efficient in training time, and able to discover clusters of varied densities. In particular, this algorithm will detect data clusters in each dimension separately, which will allow for a 2-step identification of location centers and deficient cell quality centers. For the computation of the azimuth delta, the same computation as in Baseline 2 was applied on the part of the centroid that contains the position of the high traffic cluster. In summary, in the hybrid approach, the network and spatial methods are combined. First, UEs are clustered on their spatial coordinates along with their network measurements. Then, similar to the network approach, each gNB is characterized by the network scores of its associated cluster. Finally, when a \"bad\" cell is found, it is rotated to center its direction with the geometric center of its associated clusters such as in the spatial method. **Proposed Algorithm 3:** Azimuth Steering based on Network--Spatial Clustering

**Output**: Azimuth Steering Action for each gNB

**Input**:

1. Serving gNB for each UE
2. Cluster label for each UE
3. RSRP score (low/medium/high) for each UE
4.