# Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
## 5 Performance Evaluation
### Performance Comparison  
In simulation experiments, the DDPG-LSTM algorithm proposed in this paper is compared with random offloading (RO) [45], Twin Delayed DDPG (TD3) [46; 47; 48] and local computing(Local), using the weighted sum of latency and energy consumption as evaluation criteria [36; 49]. Then, the performance of the algorithm is compared under different computational resources and task volumes, validating the performance advantages of the algorithm.  
Assume that a stack of task orders arrive at the MEC server at each slot, and the device generates a task at a slot. The task execution cost is used to compare the performance of different policies. The offload expense is the summation of the time taken by the devices to fulfil their individual tasks throughout the time slot.  
Figure 7 reveals that energy consumption and the delay weighted sum of executing the task using the DDPG-LSTM method is lower than the other three strategies. The performance of the algorithm continues to improve as the training progresses. DDPG-LSTM takes into account the satellite status information to ensure that resources are fully utilised and  
Figure 5: Training process with different batch size settings.  
Figure 6: Training process with different discount factor settings.  
continuously optimises the resource allocation strategy to ensure that the task is completed with the lowest possible latency. In addition, the figure shows that the cost of the task execution is much higher than the other strategies due to the lack of IoT device computing resources. Compared to the TD3 algorithm, DDPG has a faster convergence speed, and LSTM is easier to capture temporal information. Therefore, the DDPG-based scheme has a memory function to store valuable historical data, thus achieving better performance and validating the effectiveness of LSTM for task offloading strategies.  
Figure 8 displays the cumulative expense of different data sizes. As the task data becomes more significant, the server has to use additional time and energy to handle the tasks, and the average full system expense for handling tasks is trending up. In contrast, the DDPG-LSTM algorithm has the smaller rising trend and the better performance compared to other algorithms. As the volume of the task size becomes larger, the expense of the locally computed increases faster.  
Figure 9 indicates the changing of the cost as the LEO satellite server compute frequency rises between [3; 8] GHz. It is apparent from the figure that the combined expense of the three strategies tends to be greater as the processing frequency of the server becomes higher. As the frequency of LEO satellite server processing rises, the expense of the DDPG-LSTM suggested in this paper is the lowest.  
Figure 8: Relationship between cost and \(S_{i}\).  
Figure 7: Comparison of different algorithms.  
Demonstration of the distribution of offload tasks across several LEO satellites; Figure 10 shows four IoT devices, with three of the satellites providing edge computing capability. The altitude and computing resources of the satellites affect the mission offload, and similarly, the distribution of computing resources is affected by the mission offload. For satellite communication, transmission costs increase with altitude, and higher altitudes prolong the latency and energy consumption spent in the space segment during the computed offload. In satellite edge computing, lower computational resources and larger task offloads increase the processing latency and energy consumption of the task.