## 4 Solution Methodology
### Baseline Algorithm 1--Network Metrics Clustering
The simulator also includes a realistic user mobile terminal mobility module that can be parametrized to form hot-zone population density patterns based on the behavioral movement model, as shown in [22; 27]. This model's parameters include predefined areas of interest (denoted by geometrical area definition, average dwell time, and probability of arrival) as well as specific paths that are selected based on statistical distributions. The simulator network traffic module was based on [31] and the traffic level configuration was tuned to follow the modern traffic demand levels as shown in Table 2. For the baseline scenario in this study, we opted to analyze a cellular network area during its transition from an existing 4G cellular network to a 5G capacity layer. This deployment typically follows layered installations of gNB cells on identical site and azimuth configuration with its predecessor technology. The hexacomb placement scheme is the most common geometrical pattern and is often used as the initial topology. The simulation includes a small DU area (0.11 km\({}^{2}\)) with a high density of users (3635 mobile/km\({}^{2}\)) as well as a shopping mall zone with multiple indoor zones. The dense cell deployment requires a minimum of a 176 m inter-site distance, resulting in six gNB sites with three sector antennas Table 2. Standard traffic demand parameters were selected following the simulation scenarios used in existing 5G benchmarking studies. The mobility model of the simulation [22; 27] was parametrized with a set of areas of interest (shops, restaurants, parking lots, cinemas etc.) that are relevant to shopping malls Figure 3. Each area was linked with predefined paths according to the actual topology of the underlying area for added realism. The areas of interest were split into various categories characterized by mobility information and traffic usage levels. Areas that generate notable mobile user hot-zones are recreation areas (high mobile usage and high dwell time), which are typically coffee shops, restaurants, etc. and offline areas where high density and high dwell time was observed but mobile phone usage was limited (e.g., parking lots and cinemas). \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Simulation Value** \\ \hline Playground Dimensions & 340 m \(\times\) 340 m \\ Area (meters2) & 0.11 km2 \\ Mobile Terminals & 400 \\ Population Density & 3635 mobile/km2 \\ Total Traffic Density & 1.32 Gbps/km2 \\ Per Mobile Traffic rate & 36.25 Mbps/mobile \\ \# of gNB sites/cells & 6 (18 gNB sectors) \\ Inter-Site-Distance & 176 m (DU) \\ gNB Vertical Tilt & 26\({}^{\circ}\) \\ gNB carrier and band & 2.1 GHz (2 \(\times\) 15 MHz) \\ Radio Propagation Model & Uma 5G \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation scenario parameters. Figure 3: Specified areas of interest and example paths with respect to the site location in the existing topology. ### Simulation Measurement Dataset Analysis

In Table 3, we can see the dataset structure of the mobile terminal simulation measurements that were generated for each test case. It contains fields ranging from spatial to temporal data, IDs of the related mobile user equipment as well as the active gNB cell and the core network metrics that are related to coverage (RSRP, active RSRP), quality (SINR), and achieved data rates (throughput). It must be noted that the sampling frequency of such datasets is closely related to the rate of change that the spatial and network metric parameters have. In typical cases, such datasets have measurement intervals in the order of seconds or even lower, therefore producing a very large amount of data within a small period of time. This is something that can affect each algorithm's performance and should also be considered during the design of automated reconfiguration schemes.