# A Modular Web-Based Software Solution for Mobile Networks Planning, Operation and Optimization
## 6 Case Studies
### Network Diagnosis and Optimization
#### 6.2.1 Diagnosis Stage  
In the diagnosis stage, besides focusing on a specific geographical area, the analysis is also centered in a particular 4G carrier. As a first step, the related data is loaded into the solution, as in Figure 14.  
In the center of the figure, the circles with the darkest colors identify DT measurements with received power around \(-120\) dBm, while being the best server in that carrier frequency. From the coverage perspective, it indicates a low QoS performance, which can be fully analyzed in Figure 15.  
In the following, the aim is to bound the specific areas with low coverage, assuming a \(-120\) dBm threshold, and to identify which of the surrounding cells are the dominant cells in the low coverage areas. This can be accomplished, by selecting the cells which are more likely to be responsible for the low coverage and executing the coverage holes algorithm, as one of the diagnosis RAN algorithms integrated into the developed solution. The selection process is highlighted in Figure 16, where a map selection feature is used to select the cells contained in the green circle.  
By executing the coverage holes algorithm, specific areas, where the low coverage is both limited geographically, prevalent and consistent regarding its measured power, are identified. In this case, four individual areas were identified, being attributed to three cells, as represented in Figure 17.  
Figure 14: Visualizing the target areas with network topology and DTs.  
The colored circles identify the DT measurements that being best server signals, do not guarantee the required coverage. Moreover, the different colors pinpoint the corresponding cell by having the same color, respectively.  
Additional DT measurements also have a received power lower than \(-120\) dBm, however these tend to be sparse geographically, and are more likely a consequence of the log-normal distribution of shadow fading than actually a systematic behavior.  
However, by analysing the obtained results it can be questioned why does not the grey cell (bottom right side of Figure 17) provides good coverage. To further evaluate  
Figure 16: Selection of likely cells with low coverage.  
Figure 15: DTs received power distribution.  
this scenario, the coverage estimation algorithm (see Section 3.1) was used to estimate the received power, but it was selected to represent the respective diffraction losses, instead of the estimated received power, as in Figure 18.  
Figure 17: Result of the coverage holes algorithm for the selected cells.  
Figure 18: Diffraction losses using the coverage estimation algorithm.  
In Figure 18, it can be verified that in the area corresponding to low coverage, this cell presents high diffraction losses due to Non-Line-of-Sight (NLoS). Thus, even though the low coverage area is close to the cell stressed in Figure 18, this area must be covered by other cells.  
After diagnosing this area with low coverage, and identifying the corresponding cells by using the coverage holes algorithm, optimization stage actions are considered in the following, to improve the QoS levels.