# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
###### Abstract  
Low Earth orbit (LEO) satellite networks have shown extensive application in the fields of navigation, communication services in remote areas, and disaster early warning. Inspired by multi-access edge computing (MEC) technology, satellite edge computing (SEC) technology emerges, which deploys mobile edge computing on satellites to achieve lower service latency by leveraging the advantage of satellites being closer to users. However, due to the limitations in the size and power of LEO satellites, processing computationally intensive tasks with a single satellite may overload it, reducing its lifespan and resulting in high service latency. In this paper, we consider a scenario of multi-satellite collaborative offloading. We mainly focus on computation offloading in the satellite edge computing network (SECN) by jointly considering the transmission power and task assignment ratios. A maximum delay minimization problem under the power and energy constraints is formulated, and a distributed balance increasing penalty dual decomposition (DB-IPDD) algorithm is proposed, utilizing the triple-layer computing structure that can leverage the computing resources of multiple LEO satellites. Simulation results demonstrate the advantage of the proposed solution over several baseline schemes. s 2025 25 2892 +
Footnote †: journal: Sensors  
As the sixth-generation (6G) network continues to evolve, addressing the challenges of global coverage and ultra-low latency has become a pivotal focus for research and development. Terrestrial network edge computing can provide low-latency services for users at the edge of the network, provide feasible solutions for new applications and services spawned by IoT technology, and provide services with better data privacy protection [7,8]. However, the proportion of land covered by these services is less than 20% of the Earth's surface, and the ground network only covers densely populated areas due to economic factors. For remote areas, such as oceans, deserts, and mountains, ground edge computing services are unavailable [9]. In addition, ground computing equipment is vulnerable to natural disasters such as earthquakes and fires, as well as human activities such as terrorist attacks and power failure. Therefore, to make up for the shortcomings of the terrestrial network, using aerial or satellite networks to provide services has become a promising solution. Unlike aerial edge computing networks [10] that primarily rely on low-altitude platforms (e.g., unmanned aerial vehicles), satellite systems exhibit unique characteristics that demand tailored optimization frameworks. First, aerial networks prioritize optimizing UAV movement trajectories to dynamically minimize energy consumption under stringent onboard power constraints and rapid topology changes [11]. In contrast, satellite motion generally follows fixed orbital trajectories, allowing node positions to be calculated using orbital dynamics equations. Meanwhile, the high-speed movement of satellites induces non-negligible Doppler shifts, requiring Doppler effect mitigation in satellite edge computing frameworks--a challenge less emphasized in aerial computing systems. Furthermore, satellite networks face distinctive challenges such as intermittent inter-satellite link disruptions and periodic variations in solar power supply. These differences underscore the necessity of developing dedicated energy management strategies and latency-aware scheduling mechanisms for satellite edge computing, warranting in-depth exploration in system modeling and problem formulation. Satellite edge computing (SEC) introduces MEC into satellite networks, which enables computational tasks to be performed in space. Satellite networks equipped with MEC are called a satellite edge computing network (SECN). The SECN not only provides low-latency network services for users in remote areas, but also serves as a supplement in densely populated areas when terrestrial networks cannot fulfill user requests.