# Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks
## 2 Related Work  
In ground edge networks, there have been many works on joint computation offloading and resource allocation. [PERSON] et al. in [PERSON] et al. (2019) introduced the multi-user edge network scenario, considered the task dependency among users, and formulated it as a mixed-integer program, to optimize both task offloading and power control decisions, for the minimization of a weighted sum of energy consumption and latency. Likely, a multi-user multi-task network scenario was presented in [PERSON] et al. (2019), by formulating it as a mixed integer program and considering the service caching, computation offloading, and resource allocation, to minimize the weighted sum of latency and energy consumption. Besides, [PERSON] et al. in [PERSON] et al. (2019) introduced a multi-cell MEC-assisted network, developed an analytical model to decouple power control and computing resource allocation, and proposed heuristics. [PERSON] et al. in [PERSON] et al. (2019) studied the multi-user cooperative MEC network based on orthogonal frequency division multiple access (OFDMA), and formulated the collaborative decision making, computation offloading, and resource allocation as a mixed nonlinear program. In particular, to minimize the total energy consumption of devices, a two-stage alternating framework is proposed to decompose the collaborative problem into two layers, of which the first one is the offloading decision generation method based on an ant colony system, and the second one is the resource allocation method based on deep \(Q\) network, to obtain the optimal power control, subcarrier assignment, and computing resource allocation, given offloading decisions. Acknowledging the importance of energy efficiency (EE) optimization, [PERSON] et al. in [PERSON] et al. (2019) focused on the energy-efficient power allocation in cognitive satellite-terrestrial networks. Besides, optimal power allocation schemes for both non-real-time and real-time applications were addressed in [PERSON] et al. (2019), for optimizing the EE of cognitive satellite users. [PERSON] et al. in [PERSON] et al. (2019) introduced a power configuration algorithm based on deep Q-Learning for 5G cells, thereby optimizing both EE and throughput adequacy. Likewise, the joint power allocation and user association in wireless heterogeneous networks using the DRL was proposed in both [PERSON] et al. (2019); [PERSON] et al. (2019). However, due to technical limitations, EE optimization is not covered in the current work, and would be encapsulated in future work. Moreover, unmanned aerial vehicle (UAV)-assisted MEC begins to emerge, bringing more sophisticated computation offloading issues. [PERSON] et al. [PERSON] et al. (2019) utilized the double deep Q-network algorithm to investigate the task offloading problem in UAV-enabled MEC with the digital twin, by optimizing the mobile terminal user association, UAV trajectory planning, transmission power distribution, and computing resource allocation, thereby minimizing the system energy consumption. Likewise, the UAV-assisted MEC was also proposed to support resource-intensive applications in [PERSON] et al. (2019). More precisely, by introducing the digital twin-empowered MEC network with multiple UAVs and one ground base station, the multi-agent proximal policy optimization is used to save energy. As compared to ground edge networks and UAV-assisted MEC, the research on computation offloading and resource allocation in LEO satellite edge networks is still in the preliminary stage. Considering the high dynamics in the LEO satellite environment, how to offload tasks to nodes with abundant resources, and how to allocate resources to those offloaded tasks have become the challenges. [PERSON] et al. in [37] proposed a software-defined space-ground integrated network framework for the management and orchestration of caching and computing resources, using deep \(Q\)-learning methods. [PERSON] et al.