# On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments
## 4 Numerical Simulations and Results Discussion
### Simulations Utilizing Dataset From District in Central Europe  
In this case study scenario, we consider the use-case of adding new gNodeB nodes to the area in the city of Prague. Here, we assume that, within 3 years, the network traffic will approximately double based on the information provided in [5]. The area that was selected for that network infrastructure reconfiguration represents a typical mixed-urban environment consisting of residential areas, forests, industrial areas, etc. The list of available gNodeB nodes in the selected area consists of 75 already deployed (gNodeB/eNodeB/BTS) nodes with the peak capacity of 1 Gb/s UL; 500 Mb/s DL for each node. This information can be extracted from a freely available data source at [[https://www.gsmweb.cz](https://www.gsmweb.cz)]([https://www.gsmweb.cz](https://www.gsmweb.cz)).  
The next step was to select particular areas to cover and predict the signal strength and network traffic in those areas. The traffic had to be predicted based on the type of location since it is likely to be higher in an industry area than in a forest. First, we need to predict the signal strength in relation to each gNodeB node and each selected area (e.g., each user location). This prediction was based on the deployment use-case that is in a particular urban/suburban area. For that reason, the SUI propagation model was chosen.  
To simulate the model considering the existing gNodeB nodes the district Prague 11 was selected. This district has a total of 68,839 residents [108] (some of the residents are commuting to another district and some of them visit the district during the day, but for simplicity, we consider the size of both groups equal). Based on that data and the theoretical locations of new gNodeB nodes, we computed the optimal locations of the base stations to satisfy the new requirements due to an increasing number of connected devices with high traffic (HD video, VR, AR and others). In addition, for the computation, we expected that all the existing gNodeB nodes have a total cell capacity of 30 Gbit/s per gNodeB with a cell coverage radius of 1 km. The additional parameters for that computation are shown in Table 7.  
Using these assumptions, the dataset from the Prague 11 district was processed. The result is shown in Figure 8, where the existing gNodeB nodes are marked as blue circles and the newly added gNodeB nodes are marked as black circles. In this figure, it is shown that it would be essential to add an additional 31 gNodeB nodes to provide the network infrastructure that can deal with the increasing traffic demand.  
Based on that computation, we see that, for 5G and beyond (6G), the network operators will need to significantly modify the network infrastructure to be able to handle the increased network traffic and here our models will provide a value that can be of major use.  
In practice, the process of adding gNodeB nodes is not appropriate, and usually not even possible on a large scale. This is due to the gNodeB building process that involves a number of steps, both legal and structural. For that reason, it is advisable to add gNodeB nodes iteratively based on the current growth of network traffic with a certain reserve. In both our models, this can be expressed by just changing the input data values, e.g., multiplying the demanded user throughput to include the new applications in 5G.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Deployment** & **gNodeB** & **Single Cell** & **Demanded User** & **N. of Existing** & **N. of** & **Area** \\
**Scenario** & **Capacity [Gbit/s]** & **Radius [km]** & **Throughput (DL+UL)** & **gNodeB Nodes** & **Users** & **[km\({}^{2}\)]** \\ \hline Prague 11 (suburban) & 30 & 1 & 43.9 & 75 & 68,839 & 9.8 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Prague 11 deployment use-case.