# Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks
## 1 Introduction  
With the growth in global communications demand and the development of space Internet, connectivity to rural areas has become imperative for future networks. Since the traditional terrestrial network has limited coverage in remote areas, its infrastructure is vulnerable to natural disasters, e.g., earthquakes and floods, thus disrupting user communications [1]. Therefore, it is a prerequisite to support lower latency and more reliable communication in future wireless networks [2]. In the past few decades, satellite and terrestrial networks typically developed independently and competed with each other [3]. Although the terrestrial network is advantageous in terms of high-speed data transmission and low latency, its coverage is limited, covering only about 6% of the Earth's surface and about 20% of the land area [4]. In contrast, satellite networks are not subject to regional restrictions and can cover the globe, meeting the Internet needs in remote areas, sea and air. Besides, satellite networks have highersurvival when disasters occur, especially in earthquakes, yet also face the challenge of long-distance transmission. Therefore, both industry and academia are promoting the integration of terrestrial and satellite communications, to achieve seamless coverage and high-quality service anytime and anywhere. It is apparent that global seamless communication will be an important component in 6G networks, and thus both academia and industry have begun to discuss its requirements, application scenarios, and potential solutions [5; 6; 7; 8]. The 6G network will form a three-dimensional coverage of global communication through the interaction of satellite and terrestrial networks, forming a seamless three-dimensional coverage on a global scale, and is expected to provide heterogeneous services and seamless network coverage [9; 10]. The integrated satellite-terrestrial network architecture can integrate the information of both networks, thereby ensuring wider network coverage and higher performance [11; 12]. Yet, when providing ubiquitous and reliable services, the integrated satellite-terrestrial network also faces challenges, especially in meeting the growing quality of service (QoS) requirement. That is, with the rapid development of computing-intensive and -sensitive applications, the network has to offer a variety of computing services. More especially, users can offload part of or all computing tasks to the data center [13; 14; 15]. The data center, nevertheless, is typically built in remote areas, incurring high transmission cost and service latency, and thus failing in meeting the QoS requirements, e.g., high data rate, low latency, and low processing energy consumption [15; 16; 17; 18]. As compared to terrestrial ones, low earth orbit (LEO) satellite networks are typically deployed in the space area with an orbital altitude of 500 to 2000 km. Different from high-orbit and medium-orbit satellites, the LEO satellite's channel fading and service latency would be greatly reduced. Further, since the LEO satellite network is closer to the ground, it has lower backhaul latency and smaller channel fading, free from the ground terrain [19]. The traditional LEO satellite network is usually limited by the finite onboard capacity. Fortunately, emerging mobile edge computing (MEC) can provide services with low latency, high reliability, high security, and high flexibility by deploying computing and storage resources closer to users [20; 21; 22]. Assisted by the MEC, LEO satellite edge networks are expected to deploy MEC servers on satellites and cooperate with cloud computing data centers to further reduce energy consumption and task response latency, forming an end-edge-cloud multi-level processing architecture for different business types. That is, MEC servers on satellites can act as edge nodes to provide computing services for the ground terminal, typically with limited capacity. It is also likely that ground terminals offload their tasks to cloud computing data centers [23].