## 3 Results and Discussion
### Optimal Network  
The ENST campus was used to demonstrate the proposed framework. As the demand for internet signals is significant, the physical position of the Wi-Fi posts is critical. The geographical coordinates of the ENST campus are given in Table 2.  
As a first step, we needed to optimize the geographical positions where the Wi-Fi posts will be installed. For this, we imported a map of the ENST campus from Google Earth into the ATOLL platform. This will help identify access point locations. Signal pathway propagation, cost of renting, and availability of the sites where the equipment will be installed are critical criteria to choose access point sites. In our case, the radio sites were placed from the global positioning system (GPS) coordinates retrieved from the lighting optimization study of the solar streetlights at the ENST campus (Figure A1 in Appendix A). Once the location of the access points and their positions on the geographical map were identified, the frequency distribution for each transmitter on the network and antenna type were selected. During the dimensioning of the previously treated Wi-Fi network, an access point with specific characteristics such as the Wi-Fi compatibility standard, the transmission power, and the working frequency were selected. Afterwards, these parameters were introduced in the simulator code (Figure A2 in Appendix A). This will help estimate power received by a subscriber at each point. The predicted signal coverage for the ENST campus is shown in Figure 6. This helps assess whether the chosen locations of the access point are suitable. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Latitude** & **Longitude** & **Altitude** & **Locate** \\ \hline characteristics & 36.777736 & 3.258834 & 14 m & Algiers/Algeria \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Geographical characteristics of the site of implantation. Figure 5: Input-output of the MATLAB calculator interface for calculating the total bandwidth per cell. To assess Wi-Fi coverage, we identified five classes of signal level quality:  
* Very good coverage corresponds to a signal level higher than \(-70\) dBm. * Good coverage: corresponds to the signal values between \(-75\) dBm and \(-80\) dBm. * Low coverage: corresponds to signal values between \(-80\) dBm and \(-85\) dBm. * Poor coverage: corresponds to signal values between \(-85\) dBm and \(-90\) dBm. * Very poor coverage corresponds to a signal level below \(-90\) dBm. It can be noticed that the coverage is almost complete in the selected area and that the signal level varies between \(-90\) dBm and \(-70\) dBm. However, there are areas where the signal level is low, so there is a degradation in the coverage in these areas. This is due to the fact that the ground presents several obstacles in the direction of these locations, as is the case for some areas where the signal level is \(-105\) dBm. To minimize interference due to the use of adjacent channels and the use of the same channel, the AFP module from ATOLL was used. The AFP module was used to automatically calculate the total cost, which is the interference rate in the implanted network. The optimization was performed using the ACP module, which allows the design of networks and automatically calculates the optimal parameters in terms of coverage and quality of the signal. In Table 3, we can see the proposed increases in signal quality and coverage. From Table 3, we notice that there are four modifications: a modification in the transmission power level, a modification of the azimuth level, and the deactivation of the two sites to increase the quality of coverage. After defining the optimization and execution objectives of the ACP module, the following results were obtained (Figure 7).