LEO Satellite Assisted Edge Computing With Latency and Energy Optimization

Low earth orbit (LEO) satellite networks hold significant promise for delivering global communication services in next-generation mobile communication networks. The integration of edge computing with LEO satellite networks enables stable and reliable communication and computation services for ground user equipment (UE). This paper proposes an LEO satellite-assisted cooperative edge computing framework, where UEs and the LEO satellite collaboratively process divisible computational tasks. A system cost function is proposed to quantify both latency and energy consumption during task execution. Building on this, we formulate an optimization problem to minimize the system cost function by optimizing offloading decisions, power control, task scheduling, local computational capacity, and LEO satellite computing resource allocation. To solve this problem, we propose a discrete whale optimization algorithm with a nonlinear convergence factor and adaptive weight (NAWOA), characterized by low computational complexity. The superiority and validity of the proposed algorithm are demonstrated via numerical simulations that compare different algorithms and computational offloading schemes.