Joint Task Offloading and Beam Hopping for Satellite-Terrestrial-MEC Networks with MADRL

Mobile edge computing (MEC) supported by Low Earth Orbit (LEO) satellite communication systems is a promising approach to improve the Quality of Service (QoS) of terrestrial users. Most existing offloading methods neglect the downlink connectivity of satellites during task offloading, which may bring in extra delay and affect the offloading efficiency. This paper considers satellite-terrestrial-MEC networks in which the tasks are generated by source devices, and the computing services are provided by destination devices or the LEO satellites. Furthermore, we propose a joint task offloading and beam hopping optimization method, which aims to minimize the average time delay of all computation tasks. We formulate the joint optimization problem as a zero-one integer programming problem, which is NP-hard, and provide a multi-agent deep reinforcement learning (MADRL) framework for the intelligent task offloading scheme. In this framework, each agent is responsible for either task offloading or beam hopping, with shared rewards to enhance cooperation between agents. Additionally, to reduce the training time of traditional reinforcement learning algorithms, an attention mechanism is incorporated into MADRL. Simulation results demonstrate that our method effectively reduces the average offloading delay compared with other baseline schemes.