Task offloading and computational scheduling in RIS-assisted low Earth orbit satellite communication networks

This article investigates the joint optimization of task offloading and computation scheduling in low Earth orbit (LEO) satellite networks, where each LEO satellite is equipped with a reconfigurable intelligent surface (RIS). By considering the inherent characteristics of tasks and the energy consumption associated with task execution, we define a system utility function and formulate the problem as a constrained utility maximization problem. To address this optimization challenge, we first propose a priority-based task offloading and computation scheduling strategy tailored for single-satellite execution scenarios. Subsequently, we extend this approach to multi-satellite collaborative task execution scenarios, where a knapsack algorithm-based strategy is developed to optimize task allocation and scheduling. To underscore the advantages of the proposed RIS-assisted multi-satellite framework, we introduce a comparative analysis with a non-RIS-assisted multi-satellite offloading mode. Extensive simulations conducted in Satellite Tool Kit (STK) and MATLAB demonstrate that the RIS-assisted multi-satellite mode significantly outperforms its non-RIS counterpart in terms of system utility and energy efficiency. The results validate the effectiveness of the proposed algorithms and highlight the potential of RIS technology in enhancing the performance of LEO satellite networks.