## 4 Solutions
### Algorithm Structure
In addition, in all these comparative experiments, the disaster scenarios used are identical to the environment used in our method. As evident in Figure 9, with an increase in the number of drones providing services, the average coverage of all the methods increases until the number of drones reaches five. When the number of drones in the system is six, the number of serviced UEs decreases due to communication interference between the drones. However, it is evident that the proposed algorithm consistently outperforms the other methods in each case. In particular, when the number of drones is four, our method achieves approximately 45% higher coverage of UEs than the constant deployment method. In general, our method achieves superior performance, with an average of 10-45% higher coverage than the other methods. Figure 7: Performance of different drone numbers. ### Reward Function Analysis

Finally, we analyze our designed reward function. As indicated in Equation (10), the reward function consists of two parts: system coverage and a drone energy consumption penalty. To demonstrate the impact of the energy penalty term on the system, we simulate the reward function both with and without the energy consumption penalty term. The simulation results are shown in Figure 10.

The simulation results highlight the effectiveness of our MAPPO-based UAV control system in providing robust and energy-efficient communication services in disaster scenarios. Obviously, although Figure (b)b reveals that the coverage performance obtained using the two reward functions is comparable, Figure (a)a indicates that the network trained using the reward function with an energy consumption penalty can save approximately 3% energy. Thus, significant improvements in energy efficiency demonstrate the potential of our approach to improve disaster response efforts. Figure 8: Different drone deployment methods. Figure 5.0 Performance analysis of different methods. ## 6 Conclusions

In this study, we proposed a DRL-based approach to optimize the trajectories of UAVs in disaster scenarios to provide efficient and reliable emergency communication services. Our primary objective was to maximize the user coverage while minimizing the energy consumption of the drones. The MAPPO algorithm demonstrated robust performance in serving the rescuers in complex and dynamic post-disaster areas. Our extensive simulations validated the efficacy of the reward function designed and the MAPPO algorithm. The proposed method consistently outperformed other deployment strategies in terms of user coverage and energy efficiency. Specifically, without considering the convergence speed of the algorithm, our approach achieved an average of 10-45% higher coverage compared to the fixed-, random-, and constant-velocity deployment methods. Moreover, the consideration of an energy consumption penalty in the reward function significantly improved the energy efficiency, saving approximately 3% more energy while maintaining comparable coverage performance. The results highlight the potential of using DRL for UAV trajectory optimization in emergency communication networks. Using the adaptive learning capabilities of the MAPPO algorithm, UAVs can dynamically adjust their positions and strategies to meet the changing demands of the environment and UE distribution, thus enhancing the resilience and effectiveness of disaster response efforts. Future research directions could include exploring the integration of more advanced artificial intelligence techniques to further improve the decision-making capabilities of UAVs. Furthermore, investigating the impact of different types of environmental uncertainties, such as varying weather conditions and unpredictable obstacles, and user mobility patterns on the performance of the proposed algorithm could provide deeper insights into optimizing UAV-assisted communication networks. In addition, real-world field tests are necessary to verify the practical applicability and scalability of the proposed solutions in actual disaster scenarios.