Satellite Edge Intelligence: DRL-Based Resource Management for Task Inference in LEO-Based Satellite-Ground Collaborative Networks

Distinguished from terrestrial edge intelligence, satellite edge intelligence has unique characteristics, including the rapid mobility of satellites, limitations in computing and energy resources, and differences in the artificial intelligence models deployed on user devices, satellites, and ground cloud servers. In this paper, we propose a Deep Reinforcement Learning (DRL)-based resource management scheme for task inference in Low Earth Orbit (LEO)-based satellite-ground collaborative networks. In our approach, the task of a user can be inferred by the user device itself, the edge server of the current satellite via user-to-satellite transmission, the edge server of a neighboring satellite via satellite-to-satellite transmission, or a ground cloud server via satellite-to-cloud transmission. Our scheme jointly optimizes task offloading, computing resource allocation, and communication resource allocation to minimize the total system cost, which encompasses trade-offs among the task inference delays for all tasks, the energy consumption of system, and the task inference accuracies for all tasks, while ensuring that the transmit power budgets of all satellites and the satellite coverage time constraints for each user are met. A DRL-based algorithm combining the Softmax Deep Double Deterministic Policy Gradients (SD3) algorithm and two numerical methods is designed to solve the optimization problem efficiently. We prove the convergence of our algorithm and demonstrate the superiority of our scheme by performing extensive simulations in 4 scenarios with 4 reference schemes.