## 6 Results
### Overload Relief
Figure 14a shows the results of the naive strategy for 6 p.m., and we can see that there are more congested macrocells at this time than in the 7 a.m. period because the number of users in the region is about twice as high. This event may be related to the end of working hours of local businesses or simply that it is a Friday and there are many people in cafes, bars, and other places. The results show that the strategy was able to significantly reduce the existing overhead due to the large number of UAV-BSs requested. However, this large number of UAV-BSs, shown in Figure 14c, can significantly affect the cost of planning and potentially interfere with scheduling between UAV-BSs at other times when support is required. It also increases the possibility of interference between UAV-BSs in the region, which reduces QoS. Figure 13: Scenario with and without UAV-BS for the 7 a.m. naïve and optimized strategy. The optimized strategy applied to the 6 a.m. period shown in Figure 14b also showed similar results to the naive strategy, but the reduction in overhead was smaller due to the smaller number of supporting UAV-BS, and yet the results were interesting compared to the network resources before supporting UAV-BS. We can see the efficiency in reducing overhead, with about half of the UAV BSs being used by the naive strategy. About 3% of the macrocells continued to have resources above 80% because the user density was higher at this time than in the morning hours. Thus, the results were promising compared to the naive strategy, which used twice as many UAV-BSs. Figure 14d shows that the optimized system reduced the overhead with only 56 UAV-BSs, but 16% of the UAV-BSs were utilized above 80%, which may affect the quality of service of the users served by these devices. However, the intelligent system can use both strategies and balance the utilization of UAV-BSs by supplementing one with the other when needed. To evaluate the mobile network, the transmission rate, SINR, and user blocking probability must be analyzed as the performance evaluation of the proposed system. Figures 15 and 16 show the global throughput and global SINR average for each strategy and the network not supported by the UAV-BS, in addition to showing the number of assigned users before and after UAV-BS support. Figure 15 specifically shows the results for the 7 a.m. period, where the efficiency of the two strategies was evident compared to the network without the support of UAV-BSs. Figure 15a shows the overall throughput and proves the efficiency of the naive strategy with better results due to the number of UAV-BSs that this strategy provides. However, the difference between the naive and optimized strategies is very small, as can be seen in Figure 15b, which shows the global SINR average. Figure 15c shows the number of users connected to the mobile network before and after supporting UAV-BS, with a difference of about 100 blocked users. The results show the efficiency of the intelligent system compared to the mobile network before the support of UAV-BS. Figure 14: Scenario with and without UAV-BS for the 6 p.m. naïve and optimized strategy. Figure 16 shows the results for the 6 a.m. period, which has approximately twice as many users as in the morning. Figure 16a shows the global interference of the network before and after receiving support from UAV-BSs, comparing the two strategies with the congested network. We observed that the throughput of the congested network is relatively low compared to the two strategies that provide similar results. Figure 16b shows that the SINR is higher when the network uses the strategies to receive support, and it can obtain better QoS this way. The difference between the strategies is small, noting that the naive strategy uses twice as many UAV-BSs. Figure 16c proves that naive or optimized strategies can achieve better QoS in simulation or real experiments and also provide excellent QoE.