# PPO-Based Joint Optimization for UAV-Assisted Edge Computing Networks
## 5 Simulation Results
This is due to the ECS's ability to process tasks more quickly with greater computational power, effectively reducing the waiting latency of the edge computing network. Similarly, the proposed PPO-based joint optimization algorithm achieves a lower average delay compared to Baseline 1 and Baseline 2, which verifies the superiority of the proposed algorithm. At 1 Mbps, PBJO decreases latency by 0.3 s compared to the baseline scenarios, and the performance gap widens with increasing computing power. In addition, as the computational ability increases, we can observe that the trend of the average delay gradually flattens out. This is because the computational ability affects the waiting delay. Therefore, the significance of the delay change due to increasing computing ability gradually decreases relative to the total delay. To further examine how deployment of ECSs affects the UAV-aided edge computing network under consideration, we plotted the changes in the average latency in Figure 5  
Figure 4: Average delay vs. computing ability. Figure 3: Average delay vs. transmit power. with the number of ECSs. As anticipated, the average latency consistently decreases with an increase in the number of ECSs. An increase in ECSs will significantly enhance network computation, resulting in a reduction in the average latency. However, it is worth noting that there is a limit to the benefits of increasing ECSs. Specifically, deploying 6 ECSs only results in a reduction of around 0.2 s. When there are five or more ECSs, increasing the number of ECSs does not significantly reduce the average latency. This is because queuing is eliminated in the network when there are sufficient ECSs, and the latency of the network is mainly determined by the sending latency. The above event highlights the significance of logically strategizing the number of ECSs deployed, which is also a notable aspect of edge computing research. Furthermore, it is observed that the PBJO algorithm achieves a lower average delay than the two extreme algorithms, reaffirming its superiority. We have observed a fascinating phenomenon in which the disparity between PBJO and the other baseline algorithms initially rises and then falls in correlation with the number of ECSs. Specifically, with only one ECS, PBJO lags behind the other algorithms by a mere 0.1 s. However, PBJO boasts the most significant improvement when the number of ECSs is 2, achieving a decrease of 0.5 s. As the number of ECSs hits 6, the gap between the algorithms goes back to 0.1 s. When there are only a few ECSs, matching UEs and ECSs has minimal impact on system performance, and joint optimization brings only marginal improvement. However, as the number of ECSs increases, the computational offloading strategy plays a bigger role, and the benefits of employing PBJO become fully apparent. Further, when a large number of ECSs are present, the effect of computation offloading becomes minimal, and the gap between algorithms decreases accordingly. Additionally, we plotted the trend of average delay based on the number of UEs in Figure 6. Clearly, the average delay constantly rises with an increase in UEs. It is undeniable that a greater quantity of UEs equals a heavier load on the network, causing a higher average delay. Furthermore, we observe an interesting phenomenon where the average delay produced by the PBJO algorithm experiences a significant increase whenever the number of UEs surpasses a multiple of three. For instance, the average delay increases from 1.15 s to 1.55 s when the number of UEs rises from 9 to 10, which is 5 times greater than when the number of UEs increases from 10 to 11. This is because there are three ECSs in the simulation environment. As a result, an additional UE means that one ECS will have to manage four UEs, thus leading to a rise in the average network delay. Furthermore, the figure illustrates that PBJO exhibits a decrease of at least 0.15 s in comparison to the two baseline algorithms, thereby confirming the algorithm's efficacy. Figure 5: Average delay vs. number of ECSs.