## 5 Numerical Simulation
Figure 3: Comparison of UAV revenue with number of users [16]. Figure 5 shows how the maximum launch power of the UAV affects its revenue. With a fixed number of users, increasing the total launch power of the UAV leads to higher received power by users, resulting in lower user latency and longer charging time for the UAV to serve users, thus increasing its revenue. Similarly, when the maximum launch power of the UAV is fixed, increasing the number of users may decrease the received power by users, leading to a reduction in transmission rate and an increase in the maximum transmission latency for users, hence shortening the total charging time. However, due to the increase in the number of users, both caching revenue and charging revenue of drones also increase, resulting in an overall increase in UAV revenue. Figure 6 illustrates the impact of the total channel bandwidth of the UAV on its revenue. With a fixed number of users, increasing the total channel bandwidth of the UAV results in higher transmission rates for users, leading to reduced user latency and decreased communication transmission time. Consequently, the UAV has more time to charge users, thereby increasing its revenue. When the total channel bandwidth of the UAV is fixed, increasing the number of users reduces the bandwidth available to individual users, leading to a decrease in transmission rates and an increase in maximum user transmission delay, thus shortening the total charging time. However, due to the increase in the number of users, the caching revenue and charging revenue of the UAV also increase accordingly. Therefore, UAV revenue exhibits an overall increasing trend. Figure 4: Comparison of content provider revenue with number of users [16]. Figure 5: Comparison of UAV revenue with number of users for different UAV transmission power. Figure 7 illustrates the impact of the Zipf parameter on UAV revenue. When the number of users is fixed, as the Zipf parameter \(\alpha\) increases, the distribution of user requests for content becomes more concentrated. The UAV only needs to cache fewer files to satisfy the needs of most users, resulting in a reduction in the file leasing fees paid by the UAV. Figure 8 illustrates the impact of cache capacity on UAV revenue. As the number of users is fixed, increasing the cache capacity \(S\) of UAVs allows them to cache more content to serve users, thereby improving the cache hit rate. Figure 8: Comparison of UAV revenue with number of users for different cache capacities. Figure 6: Comparison of UAV revenue with number of users for different system bandwidths. Figure 7: Comparison of UAV revenue with number of users for different Zipf distributions. Figure 9 illustrates the sensitivity analysis results of the impact of unpredictable random movements on UAV revenue during the hovering process. In this simulation, the size of the jitter radius is used to indicate the intensity of external interference (such as strong winds) affecting the UAV. In the case of random interference, the UAV jitter radius is selected to be 0.5 m and 1.0 m. In the absence of random interference, the jitter radius is set to 0 m. As the random jitter radius increases, the probability of the UAV deviating from the optimal position also increases, leading to a decrease in UAV revenue. The simulation results show that the influence of external environment such as strong wind on the performance of the UAV is relatively large. As shown in Figure 10, the sensitivity analysis conducted on UAV altitude reveals that UAV revenue shows a trend of first increasing and then decreasing with the increase in UAV altitude. When the UAV flies at a lower altitude, the coverage area may be insufficient or the NLoS probability may be high, leading to poor channel quality. Conversely, when the UAV flies at an excessively high altitude, the path loss increases, resulting in a decrease in transmission rate. This is why the UAV's revenue at 160 m altitude in the simulation figure is better than the other two altitudes.