## 5 Performance Evaluation
### Comparative Experiment  
In this section, we compared our proposed approach with three typical solutions. As shown in Figure 7, we first studied the effect of the number of UAVs on the three important indicators of our problem. Figure 7a shows that as the number of UAVs increases from 1  
Figure 5: A set of flight trajectories in our simulation experiment. Figure 6: The trend of average reward increasing with episode. to 4, our algorithm improves the fairness index to 0.49, 0.85, 0.98, and 0.99, respectively, which is better than the other algorithms. Figure 7b shows that our algorithm improves the coverage score to 0.49, 0.83, 0.96, and 0.97, which is better than the other three comparison algorithms. This indicates that as the number of UAVs increases, our algorithm performs better in overall coverage than the other three algorithms and can achieve the near-complete coverage of users in the target area when the number of UAVs reaches 3. Figure 7c also shows that the random exploration algorithm and the greedy algorithm have a significant disadvantage in terms of lifetime compared to our proposed algorithm and the DPG algorithm. This is because the selection of random exploration actions may cause UAVs to fly out of bounds, lose connection with other UAVs, and cause the overall network to end quickly, while the greedy algorithm may get trapped in local optima due to its excessive consideration of coverage score and fairness during the exploration process. Figure 8a-c shows the effect of the increase in the number of users on the optimization problem in this article. We gradually increase the number of randomly distributed users from 10 to 40 to verify the superiority of our algorithm. Figure 8b shows that our algorithm is significantly superior to the other three algorithms in terms of coverage. As can be  
Figure 7: (**a**) Fairness index trend changing with the number of UAVs changes; (**b**) coverage score trend changing with the number of UAVs changes; (**c**) lifetime trend changing with the number of UAVs changes. seen from Figure 8c, our algorithm is not significantly different from the deterministic strategy gradient algorithm in terms of lifetime, but both Figures 8a and 8b show that the algorithm in this paper is superior to the deterministic strategy gradient algorithm in terms of coverage score and fairness index. By synthesizing Figures 7 and 8, it can be proven that the algorithm in this paper has obvious advantages over the other three algorithms in the context of simulation experiments. Based on the analysis of the above experimental results, we can conclude that the UAV actions designated by the random algorithm cannot effectively prevent a UAV from flying out of the target area or losing communication. Greedy algorithms are prone to falling into local optima, leading to the premature power outage of a UAV. Compared to the DPG algorithm, our algorithm adds two target networks, which can improve the stability and convergence of the actor network and the critic network. Meanwhile, compared to the DPG algorithm, our algorithm is more conducive to handling continuous action problems. So, our algorithm exhibits the best optimization results. Without a loss of generality, we used a dataset from the Melbourne CBD area in Australia for further experiments to verify the feasibility of the model and algorithm in  
Figure 8: (**a**) Fairness index trend changing with the number of users changes; (**b**) coverage score trend changing with the number of users changes; (**c**) lifetime trend changing with the number of users changes. this paper. We selected a 1000 \(\times\) 1000 m\({}^{2}\) area, using 20 UAVs to provide coverage services to 312 users within the area. The UAVs were evenly divided into five groups, taking off from the four corners and center points of the target area. Figure 9a,b show that using the UAV flight trajectory provided by the greedy algorithm and the random exploration algorithm, the UAV swarm ended its work in the 198 th and 207 th time slots, respectively.