Learning-based Computation Offloading in LEO Satellite Networks

Abstract

Satellite networks can provide network coverage in remote areas without terrestrial infrastructure and offer ground users an offload option. However, using satellite networks to provide computation offload services requires consideration not only of the dynamics of the satellite system, but also of how ground users offload tasks and how the limited resources of the satellite are allocated. Therefore, in this paper, we propose a computation offloading algorithm based on the optimal allocation of satellite resources (CO-SROA) and formulate an objective function to minimize the delay and energy consumption for ground users to process the computation tasks. The algorithm decomposes the optimization problem into two subproblems. One is the optimal allocation of satellite resources with determinate offloading decisions in a single time slot, which is solved based on the Lagrange multiplier method. The other is the long-term user offloading decision problem, which is solved by formulating it as a Markovian decision process and using a deep reinforcement learning (DRL) algorithm. Simulation results show that the CO-SROA can achieve better long-term returns in terms of delay and energy consumption.