# UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios
## 5 Performance Evaluation and Analysis
### Result Analysis  
First, the convergence of the algorithm is verified, as shown in Figure 3a. In the initial stage, the policy is not yet stable due to the lack of interaction between the intelligent agent and the environment, so the UAV keeps exploring and searching for ground users in the system area, and the reward value grows faster. After 700 iterations, the DRL-UCTO algorithm policy gradually stabilizes, and the reward value starts to converge and reach a high level. Second, Figure 3b compares the effect of different learning rates on the training efficiency of the algorithms by plotting the loss of Equation (23). The two pairs of curves in the figure show that the DRL-UCTO algorithm and the DQN algorithm have comparable training effects when the learning rate is 0.005. Furthermore, when the learning rate is 0.001, the DRL-UCTO algorithm possesses a higher training efficiency and can make the loss lower for the same number of training steps. It can be observed that the DRL-UCTO algorithm can quickly reach the convergence state and obtain a better reward value in the disaster area rescue scenario, resulting in an effective improvement of the system energy efficiency. In addition, if more training samples are provided for the DRL-UCTO algorithm, it may further improve the performance of the algorithm. \begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(m_{taav}\) & 9.65 kg & \(B\) & 2 MHz \\ \(f_{g}\) & 1 GHz & \(P\) & 0.1 W \\ \(f_{u}\) & 8 GHz & \(\sigma^{2}\) & \(-110\) dBm \\ \(s_{g}\) & 1000\(\sim\)2000 & \(D\) & 10,000 \\ \(\mu_{1}\) & \(10^{-24}\) & \(n\) & 128 \\ \(\mu_{2}\) & \(10^{-24}\) & \(M_{g}\) & 1\(\sim\)3 Mbit \\ \(h_{0}\) & \(-30\) dB & \(T_{g}\) & 10\(\sim\)20 s \\ \hline \end{tabular}
\end{table} 
Table 2: Simulation parameter. The speed of the transmission rate will affect the overall latency of ground users offloading computational tasks to the UAV for processing, and a faster transmission rate can make the UAV-assisted task-offloading solution more efficient. To compare the effects of different algorithms on the transmission rate between the UAV and the ground user, simulation experiments were conducted in this paper on the changes in their transmission rates with time growth for DRL-UCTO and the other two baseline algorithms, and the results are shown in Figure 4a. It can be seen that all three algorithms have a low transmission rate between the UAV and the ground user in the first 50 training episodes. After several training episodes, their transmission rates are improved accordingly. Especially the DRL-UCTO algorithm, whose transmission rate proliferates and gains a significant increase over time, followed by the DQN algorithm, while the Greedy algorithm causes the UAVs to blindly approach a certain ground user, which leads to an increase in the distance between the UAV cluster and other ground users and a slow increase in the transmission rate. The DRL-UCTO algorithm proposed in this paper can enable the UAV to find the location of ground users and establish communication faster in the disaster area, and can further improve the transmission rate between the UAV and ground users by optimizing the UAV flight trajectory. In Figure 4b, this paper further compares the optimization of the three algorithms on the system throughput. It can be seen that the DRL-UCTO algorithm still has better performance in terms of optimizing the system throughput. In contrast, the DQN and Greedy algorithms have a more limited effect on the system throughput. Figure 5a compares the performance of the three algorithms in terms of the energy consumption overhead of the computational tasks. As the number of training episodes  
Figure 4: The performance of different algorithms in terms of transmission rate and throughput. (**a**) Transmission rate of UAVs versus training episodes. (**b**) System throughput versus training episodes. Figure 3: Convergence verification of the algorithm.