# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 5 Simulation Experiment and Result Analysis
### Analysis of Simulation Results
Figure 11 shows the impact of considering different D2D clusters on the system throughput under both the DH-MDQN and DH-DQN algorithms. Three specific cases are compared: (1) where D2D clusters are present and power allocation is performed, (2) where D2D clusters are present but no power allocation is performed, and (3) where there are no D2D clusters. The simulation results show that under both algorithms, the system throughput is significantly higher when D2D clusters are present and power allocation is performed than the other two cases. Comparing the two cases without power allocation and without D2D clusters, the system throughput is improved by an average of 54% and 67%, respectively. This is due to the fact that D2D communication can significantly reduce the transmission delay and signal interference and improve the spectral efficiency through direct transmission between users in close proximity. In the case where D2D clusters exist but there is no power allocation, although D2D communication can still improve the throughput to a certain extent, the lack of power optimization may lead to an increase in interference, thus affecting the overall throughput improvement. The system throughput is generally higher in the presence of D2D clusters compared to the case without D2D clusters, which further validates the positive effect of D2D communication on system performance. In the absence of D2D clusters, the system relies on the traditional transmission mode between the base station and the user, which results in less efficient utilization of spectrum resources and limited throughput. Figure 10: System throughput comparison between NOMA and OMA with different number of sets. (5) Comparative analysis of trajectory settings under DH-MDQN algorithm. Figure 12 shows the flight trajectory of a single UAV in a scenario with six cellular users and six pairs of D2D cluster users. The overall trend of the trajectory shows that the UAV gradually approaches each part of the cellular users and D2D cluster users in the initial stage and effectively avoids interference with all types of users by adjusting its flight path in real time. Initially, as the UAV moves, it continuously adjusts its relative position to the cellular users and D2D clusters to ensure that good communication quality is maintained while avoiding interference. Eventually, the flight trajectory of the UAV converges to the optimal configuration point of the system throughput, which is capable of efficiently scheduling communication resources among multiple users, reducing resource conflicts, and ensuring the efficiency and stability of the communication link. Simulation results show that the DH-MDQN algorithm proposed in this paper is able to realize efficient trajectory planning in complex multi-UAV and multi-D2D environments, which verifies the effectiveness and practicality of the proposed algorithm in improving system throughput. Figure 13 shows the flight trajectories of multiple UAVs in 3D space and the locations of ground D2D clusters, from which it can be seen that each UAV always maintains a safe and collision-free distance from each other during flight. Thanks to the joint dynamic hypergraph modeling and multi-intelligent body reinforcement learning decision-making mechanism of the proposed DH-MDQN algorithm, each UAV can not only intelligently provide efficient communication services for the ground users and D2D clusters but also flexibly adjust the flight paths in the three-dimensional environment, which not only improves the throughput of the system but also avoids path conflicts, realizing the collaborative optimization and efficient coverage in the dynamic and complex communication scenarios, which verifies that the proposed algorithm in this paper can be used in future wireless networks. The feasibility and superiority of the algorithm proposed in this paper for the cooperative optimization of multi-UAV and D2D communication in future wireless networks is demonstrated.