On an Ultra-Dense LEO-Satellite-Based Computing Network Constellation Design

Commercial ultra-dense low-Earth-orbit (LEO) satellite constellations have recently been deployed to provide seamless global Internet services. To improve the satellite network transmission efficiency and provide robust wide-coverage computing services for future sixth-generation (6G) users, growing attention has been focused on LEO-satellite-based computing networks, to which ground users can offload computation tasks. However, how to design a LEO satellite constellation for computing networks, while considering discrepancies in the computing requirements of different regions, remains an open question. In this paper, we investigate an ultra-dense LEO-satellite-based computing network to which ground user terminals (UTs) offload part of their computing tasks to satellites. We formulate the ultra-dense constellation design problem as a multi-objective optimization problem (MOOP) to maximize the average coverage rate, transmission capacity, and computational capability, while minimizing the number of satellites. In order to depict the connectivity characteristics of satellite-based computing networks, we propose a terrestrial–satellite connectivity model to determine the coverage rate in different regions. We design a priority-adaptive algorithm to design the optimal inclined-orbit constellation by solving this MOOP. Simulation results verify the accuracy of our theoretical connectivity model and show the optimal constellation deployment, given quality-of-service (QoS) requirements. For the same number of deployed LEO satellites, the proposed constellation outperforms its existing counterparts; in particular, it achieves 25%–45% performance improvements in the average coverage rate.