# PPO-Based Joint Optimization for UAV-Assisted Edge Computing Networks
###### Abstract  
In next-generation mobile communication scenarios, more and more user terminals (UEs) and edge computing servers (ECSs) are connected to the network. To ensure the experience of edge computing services, we designed an unmanned aerial vehicle (UAV)-assisted edge computing network application scenario. In the considered scenario, the UAV acts as a relay node to forward edge computing tasks when the performance of the wireless channel between UEs and ECSs degrades. In order to minimize the average delay of edge computing tasks, we design the optimization problem of joint UE-ECS matching and UAV three-dimensional hovering position deployment. Further, we transform this mixed integer nonlinear programming into a continuous-variable decision process and design the corresponding Proximal Policy Optimization (PPO)-based joint optimization algorithm. Sufficient data pertaining to latency demonstrate that the suggested algorithm can obtain a seamless reward value when the number of training steps hits three million. This verifies the algorithm's desirable convergence property. Furthermore, the algorithm's efficacy has been confirmed through simulation in various environments. The experimental findings ascertain that the PPO-based co-optimization algorithm consistently attains a lower average latency rate and a minimum of 8% reduction in comparison to the baseline scenarios. edge computing; unmanned aerial vehicle; proximal policy optimization; joint optimization 2023131282  
and prolonged waiting latency, consequently reducing edge computing network throughput. Within the confines of limited edge servers and deployment locations, enhancing the flexibility of the edge computing network while effectively offloading computing tasks poses a significant challenge for the current state of edge computing. It should be noted that, in edge computing, deployable ECSs play a crucial role. However, due to the high cost of hardware, it can be challenging to meet the business experience needs of all users with a limited number of ECSs deployed in fixed locations. This is especially true when wireless links are obstructed by buildings, mountains, and other obstacles, severely affecting the wireless link between user terminals (UEs) and ECSs. In recent years, unmanned aerial vehicles (UAVs) have been deployed in wireless communication networks due to their advantages in mobility and cost. UAVs can be used as mobile relay nodes to facilitate information exchange between distant users and as mobile base stations to enhance the coverage range of wireless networks. By leveraging the wide coverage, low cost, and flexibility of UAVs, they can be used as relay nodes in edge computing applications to effectively address the issue of degraded link quality, thereby ensuring a satisfactory user experience at the UE. However, the performance of UAV-assisted edge computing is directly related to the deployment location. To ensure a good user experience in edge computing, the deployment location of UAVs needs to be precisely designed. Furthermore, the incorporation of UAVs will impact the selection of deployment tactics, which is a convoluted process. As opposed to conventional terrestrial mobile edge computing networks, the integration of unmanned aerial vehicles enhances network flexibility, yet simultaneously introduces fresh obstacles to network deployment and decision-making. Joint optimization of computation offloading and drone deployment in drone-assisted edge computing networks, considering heterogeneous user demands and resource constraints, will be critical for enhancing the performance of edge computing networks. Currently, many resource allocation problems in mobile communication networks are primarily solved using convex optimization, game theory, and other methods. Typically, these methods require the objective function or problem model to conform to specific forms.