## 4 Solution Methodology
### Baseline Algorithm 1--Network Metrics Clustering
In this study, we focused our efforts on a case of 5G deployment for the 2.1 GHz band, however, it is in the scope of the continuous technology rollout that millimeter-wave carrier frequencies will be utilized, as seen in various studies [1]. Radio transmission at higher frequencies tends to benefit less from wave spreading phenomena such as refraction in comparison to the core cellular frequencies (0.5-3.5 GHz range). Millimeter-wave transmission is very sensitive to cell antenna direction and line of sight plays a critical role on the quality of the link. It will be interesting to expand this research toward applying the proposed schemes on millimeter and even micro-wave carrier experimental deployments and compare the benefits with respect to this study. Traditional model-based approaches for optimum configuration can be shown to be ineffective in areas with very high concentration of mobile phone users, therefore, we believe that data-driven approaches are key to optimized deployments. Automated reconfiguration algorithms are effective if used with accurate measurements of the network location information of the mobile terminals. It greatly reduces the time and effort required from processes such as continuous manual analysis and reconfiguration performed by teams of radio engineers. Simulation software is also a possible alternative that can be used to replace real measurements, if careful considerations and scenario design have been made, which in turn would require expert knowledge on telecommunications and user behavioral analysis in various urban contexts. Automated azimuth steering methodologies are shown to effectively utilize unsupervised learning components such as the K-means,

Figure 12: Comparison between the different algorithms’ improvement vs. the baseline for per user normalized throughput gain (Mbps). X-means, DBSCAN, and other variants. These algorithms generate robust results that are a good fit to the statistical distributions of network data measurements. Clustering label traceback analysis is also an important component, as shown in the implementation of Baseline 1 and the proposed algorithm. It is based on conditional probability analysis to create network metric labels that provide meaningful insights for the cells. ## 7 Conclusions

In this paper, we designed and tested data-driven methodologies that allow for automated coverage and performance maximization of the 5G capacity layer. This approach is suited for areas that include hot-zone population densities and network traffic patterns mostly found in dense urban and urban zones. The algorithms consist of a combination of unsupervised learning, statistical analysis, and analytical coverage optimization, based on measurements performed on mobile terminals from an initial, hexacomb-based symmetrical network deployment. These measurements include temporal, network, and location information, which are utilized differently by each included algorithm. The outputs of these algorithms are azimuth angle changes in various cells of the underlying network that maximize the RSRP, SINR, and per user downlink throughput. Specialized simulation software for 5G radio simulation was used as the validation platform of these algorithms with realistic mobility patterns and radio environments that are found in dense urban shopping mall zones. All algorithms generated acceptable results that improved the network coverage and quality in relation to the reference scenario. The two baseline algorithms underperformed by a significant factor compared to the proposed scheme in both metrics due to their lack of information to correctly specialize to the zone's characteristics. In particular, the network metric clustering approach (Baseline 1) can only use the sector's geometry to estimate the traffic hotspot location, which leads to incorrect actions. Baseline 2 algorithm, on the other hand, utilizes the location information to identify population density hot-zones.