## 4 Solution Methodology
### Baseline Algorithm 1--Network Metrics Clustering
SINR score (low/medium/high) for each UE

**Begin**

_Cells \(\lnot\)_ Set of Serving gNBs

_Clusters \(\lnot\)_ Set of Cluster labels

**Foreach**_Cluster_ in _Clusters_**do**:

1. Cluster RSRP score \(\lnot\) majority RSRP of _Cluster's_ UEs
2. Cluster SINR score \(\lnot\) majority RSRP score of _Cluster's_ UEs

**End**

**Foreach**_Cell_ in _Cells_**do**:

1. Find the Subset of Clusters with low SINR and low RSRP scores 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

**End**

**End**

**Algorithm 3** Azimuth Steering Action for each gNB

### Complexity Analysis

The use of HDBSCAN [20; 25] offers the advantage that the number of clusters does not have to be configured such as all density-based clustering algorithms. Another great property of the algorithm is that when the feature space of the training data is not very big such as in our case, its time complexity is O(_nlogn_), which is sub-quadratic and very competitive to K-means. In our experiments, grid search included approximately ~100 parameter configurations and each configuration lasted less than 2 min for the most cases, with the whole procedure finishing in a few hours. It should be noted that the most time-consuming configurations were the ones with high epsilon values (cluster_selection_epsilon), so these should be treated with caution. ## 5 Performance Evaluation

System-level simulators compatible with the latest 4G and 5G radio access modules have been used throughout the literature for testing and validation of various cellular network optimization schemes. They are also used by radio planning and optimization engineers during the process of manual parameter reconfiguration. We performed our study in a validated 4G/5G cellular network simulator for both the initial reference scenario and the optimized configurations generated by each algorithm. We designed the simulation scenario to include a number of traffic hot-zones that can be found in areas such as a shopping mall placed in a dense urban zone. This type of cellular network zone presents with complex mobility patterns due to the different areas of points of interest within the venue. The evaluation process will start with measurements in the initial scenario for the establishment of the network performance baseline, then for each algorithm, we will perform the execution based on the measurement data, which will result in new scenarios that will include the reconfiguration. We will then perform separate simulation executions generating new network measurements for each reconfiguration scheme. The results will be analyzed by means of statistical distribution analysis on coverage (RSRP) and quality (SINR and user downlink throughput) to measure the benefits of each algorithm and compare them. We will also comment on the expected vs. actual results based on each algorithm's design principles in conjunction with the selected scenario. ### Simulation Platform Overview and Scenario

The simulation software used for this study was a 5G cellular network simulator [22; 27] based on 3 GPP specifications [28; 29; 30] that can be used for macro-simulation of network functionalities and user mobility scenarios of 4G and 5G cellular networks. The radio channel component supports multiple cellular network elements such as LTE eNodeB, Pico cells, and NR gNB. It is based on standard-based propagation models specified in [28; 29] for various urban environments. Its radio quality model translates the link quality (SINR) to the achieved throughput, which is used to serve the generated traffic of the mobile terminals. It also includes a network planning module that was used to automatically generate the initial hexacomb topology. It can easily apply the reconfiguration actions of the selected algorithms to create new measurements based on the impact of the produced azimuth reconfiguration.