## 5 Algorithm Performance Evaluation and Comparison
### Simulation Result Analysis  
Firstly, to verify the convergence performance of the MADDPG and other baseline algorithms in this scenario, a total of 500 training episodes were set. Figure 4 shows the convergence of the total system cost under the different algorithms, with the horizontal axis representing the number of training episodes and the vertical axis representing the total system cost. The All-local and All-offload algorithms remain relatively stable, while the system cost achieved by the Random algorithm is unstable due to its randomness and requires a long period of training to yield convergence. The D3 QN algorithm shows relatively large fluctuations and takes a longer training time to achieve convergence. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Number of M-IoT devices & 3 \\ Number of UAVs and LEO satellites & 5, 1 \\ Bandwidth \(B_{ij}\) & 1 MHz \\ Noise power \(N_{0}\) & \(-\)100 dBm \\ Transmission power of \(\mathrm{M}-\mathrm{IoT}\) devices \(p_{ij}^{u}\) & [1, 3] W \\ Maximum transmission power \(\mathrm{p_{1}^{max}}\) & 3 W \\ Task data size \(D_{i}\) & [1, 5] MB \\ Maximum tolerable delay \(T_{i}\) & 0.5 s \\ Local computing capability \(f_{i}^{t}\) & 1 GHz \\ Edge server computing capability \(f_{ij}^{u}\) & [1, 5] GHz \\ Cloud server computing capability \(f_{i}^{s}\) & 15 GHz \\ Energy efficiency coefficient \(\kappa_{i}\) & \(10^{-27}\) \\ Weight factors \(\omega_{T}\), \(\omega_{E}\) & 0.5, 0.5 \\ UAV flight altitude \(H\) & 100 m \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation parameter setup. Figure 4: Comparison of convergence across different algorithms. Compared to these baseline algorithms, the MADDPG algorithm performs the best. Specifically, in the early stages of iteration, the MADDPG algorithm experiences significant fluctuations. This is mainly because, during the initial learning phase, the neural network is in the exploration stage, with the agents yet to collect sufficient experience samples from the replay buffer, leading to considerable instability. As the number of training iterations increases, the agents continuously interact with the environment, exploring the impact of offloading locations on the reward function. They consistently search for the optimal offloading strategy and learn effective computation offloading and resource allocation policies, achieving the goal of reducing the total system cost. After 400 training episodes, the MADDPG algorithm tends to stabilize, primarily due to the centralized training and distributed execution strategy adopted by MADDPG. All the agents can share each other's state observation information, enabling them to make more informed decisions. Compared to traditional reinforcement learning methods, this technique is more suitable for dynamic and complex environments, allowing for flexible offloading and resource allocation decisions based on user mobility, thus reducing the total system cost. Secondly, with the other parameters kept constant, the variation in the total system cost under different numbers of M-IoT devices was verified, as shown in Figure 5. The horizontal axis represents the number of M-IoT devices, while the vertical axis represents the total system cost. In this experiment, the M-IoT device quantities were set to 4, 6, and 8. As the number of devices increases, the total system cost also increases. Figure 5 shows that the All-local algorithm incurs the highest system cost. This is because the computational capacity of the local devices is limited, leading to high system costs due to restricted device computing resources. Due to the randomness in selection, the Random algorithm yields inconsistent strategies, resulting in fluctuating system costs that lack stability. Figure 6 illustrates the impact of different numbers of MEC servers on the total system cost, with the horizontal axis representing the number of MEC servers and the vertical axis representing the total system cost.