Resource Allocation in LEO Satellites assisted Terrestrial Network for Latency-Sensitive Task

Low Earth Orbit(LEO) is the supplement of the terrestrial network, it has the advantages of low latency and high bandwidth compared to Geostationary Earth Orbit (GEO). LEO can cover and provide the service to the unconnected areas in terrestrial. Limited to the computing capacity of LEO satellites, LEO is not sufficient to process the terrestrial tasks, hence, a LEO-assisted central cloud to provide services is the promising method. The latency-sensitive task has stringent requirements of latency, and LEO has a low attitude and serves the low latency tasks. In this paper, we propose an LEO-assisted terrestrial network architecture and the tasks offloading and resource allocation are also investigated. We present a deep reinforcement learning approach as the solution for task offloading and resource allocation aiming at minimizing energy cost. The results show that our proposed Soft Actor-Critic has a superior performance than other benchmarks and about 51% energy reduction compared to Proximal Policy optimization (PPO).