A new study published in Engineering presents a refined approach to designing ultra-dense low-Earth-orbit (LEO) satellite constellations tailored for satellite-based computing networks. As demand grows for seamless global internet and edge computing services, especially for future sixth-generation (6G) networks, the research offers a method to optimize satellite deployment while balancing coverage, computing capacity, and transmission performance.

The paper, titled On an Ultra-Dense LEO-Satellite-Based Computing Network Constellation Design, focuses on enabling ground users to offload computational tasks to satellites equipped with edge computing servers. Unlike traditional satellite networks that merely relay data, these LEO satellites are capable of processing tasks onboard, offering a promising solution to the limitations of terrestrial infrastructure in remote or high-traffic areas.

To address the challenge of designing a constellation that meets diverse regional computing demands, the authors formulate the satellite deployment problem as a multi-objective optimization problem (MOOP). The goal is to maximize average coverage rate, transmission capacity, and computational capability, while minimizing the total number of satellites. The study introduces a terrestrial–satellite connectivity model to evaluate how well different regions are served, considering the dynamic geometry between orbiting satellites and Earth-bound users.

The proposed constellation model uses an inclined-orbit configuration, specifically a Walker-delta pattern, which allows for flexible adjustment of orbital parameters to better serve middle–low latitude regions. These areas are prioritized due to their higher population density and corresponding offloading demands. The authors develop a priority-adaptive inclined-orbit constellation design (PA-ICD) algorithm to solve the MOOP, enabling optimal configuration of orbital altitude, inclination, and satellite distribution based on quality-of-service (QoS) requirements.

Simulation results show that the proposed constellation outperforms existing LEO networks such as Kuiper, Telesat, OneWeb, and SpaceX in key metrics. For the same number of deployed satellites, the new design achieves 25%–45% improvements in average coverage rate. The study also demonstrates that the theoretical connectivity model aligns closely with simulations conducted using the Systems Tool Kit (STK), with a discrepancy of approximately 1%, while offering computational speeds over 1,000 times faster.

The authors further explore the impact of orbital parameters on network performance. They find that adjusting orbital inclination and altitude can significantly influence coverage distribution, with optimal values depending on the latitude of the target region. For instance, concentrating coverage in populated low-latitude areas requires a higher orbital altitude and lower inclination, which also helps reduce the total number of satellites needed.

Additionally, the study introduces a dual-layer constellation design to extend coverage to high-latitude and polar regions. This approach combines a high-inclination orbital layer with a low-inclination one, ensuring global service availability while maintaining cost efficiency. Compared to the Telesat constellation, the proposed dual-layer system reduces deployment costs by 24.8%, offering a more economical solution without compromising performance.

The research contributes a practical and scalable framework for designing LEO satellite constellations that support computing-intensive applications. By integrating communication and computational performance into the design process, the study addresses a gap in existing literature, which often focuses solely on satellite communication. The findings offer a foundation for future satellite internet systems aiming to support distributed computing in 6G networks and beyond.

The paper “On an Ultra-Dense LEO-Satellite-Based Computing Network Constellation Design,” is authored by Yijing Sun, Boya Di, Ruoqi Deng, Lingyang Song. Full text of the open access paper: https://doi.org/10.1016/j.eng.2025.06.007. For more information about Engineering, visit the website at https://www.sciencedirect.com/journal/engineering.