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

The hybrid network-spatial clustering's goal is to simultaneously detect clusters with deteriorated radio quality and signal strength as well as identify their exact location using the location centroid components. Based on this, we expect it to produce the best possible azimuth steering actions that will maximize the effectiveness of the gNB's antennas. The results of the algorithm execution are shown in Figure 6. They include both location and clear indication of the network metrics for each centroid. Using this information, the algorithm filters the centers into a smaller set that only includes actual traffic hot-zones. ### Algorithm Performance Evaluation

The goal of each algorithm in this benchmarking is the maximization of coverage (RSRP) and quality (SINR) of the total cellular network area. In addition, we will evaluate the achieved, per-device downlink throughput to monitor the impact of the changes in the achieved quality of service. For this purpose, we used the statistical cumulative distribution functions of these KPIs as well as the 25 th, 50 th, and 75 th percentile to acquire quantitative results on the improvements with respect to the reference scenario. To evaluate the distributions [2,3,6], we are looking for right shifts toward higher RSRP/SINR/normalized per user downlink throughput values, which would indicate network metric improvement. In Figure 7, it is evident that the RSRP distribution shifted to the right from 2-5 dB in various ranges of the distribution for all of the optimization schemes. The spatial and network KPI clustering seemed to improve the reference scenario by \(\sim\)1 dB, which is acceptable,

Figure 5: Spatial clustering generated zones (**left**) and centers (**right**). Figure 6: Hybrid network–spatial centroids colored by their SINR (**left**) and RSRP (**right**) metrics. however, the hybrid clustering approach improved by more than 2.5 dB, making it the best candidate solution for the coverage optimization goal. The same distribution analysis was then performed on the SINR [Figure 8] measured by the mobile terminals during their transmission. RSRP gains alone are not adequate measures of quality improvement, mostly due to the possibility of increased interference caused by overlapping antenna sectors. In the analyzed data, we could see that the SINR had also improved by all algorithm schemes, therefore indicating that the radio quality had not deteriorated. The proposed scheme's distribution was still a clear improvement vs. the two baseline algorithms by a factor of 2-4 dBs, proving that it can correctly capture the interference constraints of the network as well as specialize to the locations of high traffic density. The same benefits of the proposed scheme are also visible in the statistical analysis of the normalized throughput distribution, as seen in Figure 9. It is clear that higher normalized throughput ratio values receive more measurement density in the case of the proposed scheme. Figure 7: Performance evaluation of each algorithm performance vs. the initial scenario for RSRP (dBm) CDF. Figure 8: Performance evaluation of each algorithm vs. the initial scenario for SINR (dB) CDF. Figure 9: Performance evaluation of each algorithm vs. the initial scenario for normalized throughput (ratio vs median) CDF. ## 6 Discussion

Optimum 5G capacity layer deployment involves a large number of parameters to be considered and tuned, however, azimuth steering can provide significant improvement in coverage and quality with the minimum amount of effort and cost. The azimuth steering problem is shown to be very closely related to the geometry of the active users of the underlying network. The directional gain of the sector antenna (or patch antennas for gNB) provides significant improvement in RSRP, SINR, and achieved user throughput, even in non-line of site propagation (which is very highly present in modern environments).