# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 5 Simulation Results and Discussion
### Performance Evaluation of UAV Dynamic Deployment Algorithms
\begin{table}
\begin{tabular}{c c} \hline \hline
**System Parameters** & **Value Range** \\ \hline Number of mobile devices & [10; 15; 20; 25; 30] \\ Number of UAVs & 3 \\ Discount factor \(\gamma\) & 0.95 \\ Decay factor \(\lambda\) & 0.9 \\ Training episode & 200 \\ Critical learning rate & 0.02 \\ Actor learning rate & [0.001, 0.002, 0.01, 0.1] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main simulation parameters. Figure 3: Comparison of the trend of prediction error of each algorithm with the number of training rounds. of UAVs, and improving the stability and the effectiveness of mission execution under uncertain and dynamic conditions. As shown in Figure 5, in the same sea area, with the increase in user density, although the UAV can reach the coverage condition more easily, the coverage rate of users also shows a decreasing trend due to the relative increase in the number of users distributed at the edge. The PPO algorithm shows relatively strong stability in the change of coverage under different numbers of users, and its coverage decreases from 95% with 10 users to about 80% with 30 users. Despite the decrease, the performance is better compared to the Random and DDPG algorithms. The PPO algorithm can maintain a high coverage rate more stably with the increase in the number of users and has relatively good adaptive ability. In contrast, the Random algorithm, due to the lack of policy optimization, cannot respond effectively in the face of more users, resulting in a significant drop in its performance. As a result, Random performs poorly when the number of users increases, and its coverage gradually decreases to about 72% with the increase in users. Due to the interference of noise, the DDPG algorithm's policy update is more unstable, and its coverage performance is worse as the number of users increases, dropping from 90% to less than 70%. RUDD adds a prediction algorithm to the PPO, which can optimize the policy in advance, making it able to maintain a higher coverage in the face of the dynamically changing marine environment of the users, and showing great stability and adaptability. As a result, the RUDD algorithm has strong robustness as the coverage rate only drops from about 98% to 85% even with the increase in the number of users. As shown in Figure 6, within the same sea area, as the user density increases, the UAVs can meet the coverage requirements with relative ease, which in turn leads to a reduction in the total flight delay. This results in a decreasing trend in the average user delay. In different user size scenarios, both DDPG and Random algorithms show a significant decreasing trend as the number of users increases. However, under the constraints of UAV computing power, battery capacity, and coverage area, although the latency of these two algorithms decreases rapidly, due to the limitations of the algorithms themselves, their latency is still higher than that of the RUDD algorithm and the PPO algorithm when the number of users reaches 30. Meanwhile, the PPO algorithm can achieve the best effect of the algorithm more stably by its effective control of the magnitude of policy updates. Based on PPO, we introduce a prediction algorithm, which allows UAVs to quickly make deployment strategies based on the distribution state of users. As a result, the RUDD algorithm has the best performance among all the algorithms, with an average response delay of 1s and the smallest curve variation, which significantly improves the overall execution efficiency and stability. The low-latency performance of the RUDD algorithm ensures that the UAV achieves fast response and stable coverage in maritime communication, which greatly improves the safety and efficiency of maritime operations. Figure 4: Comparison of total reward values for different models. As shown in Figure 7, as the number of users increases, the flight energy consumption and hover energy consumption decrease due to the decrease in the average delay of users. Thus, the average energy consumption of users also shows a decreasing trend.