# PPO-Based Joint Optimization for UAV-Assisted Edge Computing Networks
## 2 Related Work  
### State of Art  
_Edge Computing_: Currently, research into the application of mobile edge computing has received significant attention [8; 9; 10; 11]. The work in [9] investigates computation offloading and resource allocation issues in single ECS scenarios. The work in [12] proposes an online joint radio and computation resource management algorithm for the scenario of multiple mobile devices and a single ECS. By optimizing power and bandwidth al location, it minimizes the long-term average weighted sum of mobile device and ECS power consumption. The work in [8] studies distributed computation offloading in a wireless-powered, multi-user, single-edge computing system. A game-theoretic computation offloading scheme is constructed to optimize the weighted sum of energy harvesting and offloading delay. Moreover, a two-stage joint optimization scheme was proposed for mobile edge computing using federated learning in [13]. Similar to federated learning, the work in [14] presents an edge computing collaboration and offloading optimization problem for underwater sensor networks. The study designed an artificial neural network model to solve this issue. When exploring the use of mobile edge computing in vehicular networking, several sources have investigated the associated challenges. For example, the work in [15; 16] has focused on mobility issues specifically related to vehicular networking edge computing, proposing solutions aimed at addressing the demands of latency-sensitive and computation-intensive in-vehicle applications. Additionally, the work in [11; 17; 18; 19; 20] examines concerns surrounding load computation and resource allocation in multi-ECS scenarios. However, it is important to note that geographical limitations may impede the establishment of wireless connections between UEs and ECSs. The aforementioned works offer a comprehensive exploration of conventional terrestrial edge computing networks. Further research on mobile edge computing networks is required, with a primary focus on the wireless channel quality and various practical constraints. _UAV-Assisted Edge Computing_: To address the issue of wireless connectivity arising from fixed deployment setups, UAVs have been incorporated into mobile edge computing due to their adaptable deployment attributes. There have been studies conducted on the viability of this approach. In the research into ECSs with UAV-assisted offloading for ground networks, the existing works primarily focus on addressing the energy consumption issues of UAVs. [PERSON] et al. [21] propose an ECS model based on UAVs, where a computing-capable UAV provides computation offloading services to ground systems with limited local data processing capabilities. They employ a successive convex approximation approach to minimize the energy consumption of ground applications. [PERSON] et al. [22] develop an edge computing system that enables wireless power transfer to UAVs and address the causality between computing and energy transfer under power minimization constraints. [PERSON] et al. [23] utilize UAVs as computing servers to provide services to ground terminal devices and act as relays to offload tasks from terminal devices to access node computing. By employing an alternating optimization algorithm, they achieve the minimization of the total energy consumption of devices and UAVs. [PERSON] et al. [24] consider a three-tiered architecture for a UAV-assisted offloading edge computing system and obtain the optimal solution for the optimization objective using Lagrange duality. In addition, Refs. [25; 26; 27] investigate the deployment of UAVs while keeping in mind the energy consumption of edge computing networks. The current research places greater emphasis on the energy consumption of the network, whereas the investigation into latency, a crucial factor for meeting user requirements, requires further development.