# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 5 Simulation Experiment and Result Analysis
### Analysis of Simulation Results
Among them, the DH-MDQN algorithm shows the highest system throughput at all D2D cluster sizes; in particular, when the number of D2D clusters is 20, the throughput increase is the most significant. When the number of D2D clusters is small, the allocation and optimization of system resources is relatively simple, and the throughput grows faster at the initial stage; however, as the number of D2D clusters increases further, the interference in the system also increases, resulting in a flattening out of the throughput increase. For different numbers of D2D clusters, the system throughput of the DH-MDQN algorithm is about 13% higher than that of the DH-DQN algorithm on average; in particular, when the number of D2D clusters is 30, the DH-MDQN algorithm produces a system throughput of about 48,400 Kb. This further verifies the significant advantage of the DH-MDQN algorithm in enhancing the system throughput at different numbers of D2D clusters. Figure 9 demonstrates the comparison of system throughput at different D2D cluster communication distances. As the communication distance increases, all system throughput shows a certain trend. It can be observed that when the communication distance of the D2D cluster is  
Figure 8: Comparison of system throughput with different numbers of D2D clusters. small, the system throughput is higher, which is because the shorter communication distance can reduce the signal attenuation and interference, which makes the resource allocation more efficient and the transmission rate better guaranteed. As the communication distance increases, the signal attenuation and interference gradually increase, resulting in a decrease in system throughput. In addition, the performance of different algorithms at different communication distances varies. For different D2D cluster communication ranges, the system throughput of the DH-MDQN algorithm is about 15% higher than that of the DH-DQN algorithm on average; in particular, when the D2D cluster spacing is 10 m, the DH-MDQN algorithm produces a system throughput of about 42,900 Kb, which suggests that the DH-MDQN is able to better balance the resource optimization and the interference management, especially in medium-distance scenarios, and the advantages are fully reflected. While other algorithms, such as G-MDQN and MDQN, can achieve better performance at small distances, the decreases in throughput and rate are more obvious at larger distances, which indicates that they are less capable of handling large-scale interference. (3) Comparative analysis of multiple access under DH-MDQN algorithm. In order to illustrate the impact of NOMA as well as power allocation on system throughput, Figure 10 shows the comparison of the system throughput under both algorithms, NOMA, OMA, and NOMA without decoding order constraints, for both DH-MDQN and DH-DQN. It can be seen that the throughput of each algorithm gradually improves as the number of training times increases, indicating that the model gradually converges in continuous training. The simulation results illustrate that removing decoding order has a significant effect on the system throughput in the case of NOMA. Compared to the case without decoding order constraints, the system throughput improves by 34% on average. This shows that decoding order plays a key role in NOMA. The absence of decoding order constraints may lead to less efficient inter-user interference handling, which further affects the performance of the decoding  
multiplexing, which significantly improves the spectrum utilization, and thus the system throughput is much higher than that of OMA. In contrast, OMA is limited by the adoption of an orthogonal resource allocation strategy, where the resources are allocated independently among different users, which restricts the resource utilization efficiency of the system and results in a limited growth of its throughput. (4) Comparative analysis of D2D under DH-MDQN algorithm.