# DNNs Based Computation Offloading for LEO Satellite Edge Computing
## 2 Related Works  
In recent years, satellite edge computing technology and artificial intelligence technology have received a lot of attention. In this section, we will introduce the relevant literature. [PERSON] et al. [10] propose the \"LEO-MEC\" scenario, that is, placing edge servers on LEO satellites. In the literature, the authors mainly consider two problems: one is the service request scheduling decisions problem for the resource utilization of the system. Another is the service placement problem related to scheduling decisions. The authors use the OPTI toolbox to solve this problem, which achieves better performance than the contrasting algorithms. [PERSON] et al. [11] propose a structure that combines satellite edge computing with cloud computing, defining the optimization problem as a problem of reducing system energy consumption, and using a distributed algorithm to obtain satisfactory system energy consumption. [PERSON] et al. [12] apply virtual network function (VNF) technology to the scenario of satellite edge computing, define the VNF placement problem as a game problem, and propose an algorithm based on game theory to solve the placement problem, so that the entire system can obtain maximum benefit. [PERSON] et al. [13] study the allocation of computing resource and communication resource for computing tasks in satellite networks. For this complex mixed integer programming problem, a low complexity algorithm based on game theory and many-to-one matching theory is proposed, which greatly reduces the tasks execution delay. Because Space-Air-Ground Integrated Network (SAGIN) can provide full coverage on a global scale and can efficiently manage resource, a lot of literature considers combining SAGIN with edge computing. [PERSON] et al. [14] introduce a scenario combining edge computing under the edge computing-enabled SAGINs (EC-SAGINs) architecture. In some special extreme scenarios, it can provide users in remote areas with Internet of Vehicles services. In addition, this document proposes a pre-classification scheme and a deep imitation learning (DIL)-based decisions algorithm to enable the satellite to complete the tasks as quickly as possible under the premise of minimum resource usage. [PERSON] et al. [15] study the tasks offloading scheme of ground users in the SAGIN architecture. As the environment in the SAGIN architecture is constantly changing, this document proposes a tasks scheduling algorithm based on reinforcement learning to adapt to the changing environment. Through the reasonable allocation of resource, a better user's QoE is obtained, and the convergence of the system is also improved. [PERSON] et al. [16] propose an aerospace-assisted hybrid cloud edge computing framework, which minimizes the maximum computing delay between system end-users through joint optimization of the system's communication resource and computing resource. Under the premise of ensuring convergence, this document proposes an alternate optimization algorithm to optimize the system, which has achieved better convergence and lower computational delay. As an important technology in the field of artificial intelligence, deep learning has received more and more attention. People have applied deep learning to many different fields and obtained many achievements [17; 18]. In wireless communication, deep learning also solves many computational problems [19], such as resource allocation [20; 21], signal detection [22; 23], interference alignment [24] and caching [25]. Some recent studies have applied the idea of deep reinforcement learning (DRL) to the MEC system, solved the problem of computing offloading and resource allocation of the MEC system, and made some progress. In [26], [PERSON] et al. use deep reinforcement learning to solve the joint optimization problem of computing offloading and resource allocation in MEC systems. In [27], [PERSON] et al. propose a DRL-based model-free collaborative computing offloading and resource allocation scheme, which achieves the optimal system performance.