A Deep Reinforcement Learning–Based Low Earth Orbit Satellite-Enhanced Mobile Edge Computing Framework for Efficient Task Off-Loading

Abstract

With the rapid advancement of the Internet of Things (IoTs) and 6G technologies, traditional terrestrial networks are becoming less capable of supporting demanding computational tasks. This limitation stems from their restricted coverage and poor adaptability to changing environmental conditions. Low earth orbit (LEO) satellite networks offer global coverage. However, existing mobile edge computing (MEC) frameworks struggle with unstable links, high decision complexity, and limited real-time performance. To overcome these challenges, this paper proposes a LEO satellite-enhanced MEC off-loading architecture based on improved multiagent deep reinforcement learning (MADRL). By integrating ground terminals, LEO satellite edge servers, cloud servers into a three-tier collaborative system, and introducing an independent Q-value mechanism, the proposed method jointly optimizes task off-loading and resource allocation in dynamic environments. This design reduces algorithm complexity and enhances decision flexibility. Experimental results show that the proposed method outperforms baseline approaches in end-to-end latency, energy efficiency, and convergence speed, while maintaining robust performance under varying satellite densities and user workloads. These results demonstrate the potential of the proposed approach for efficient task off-loading in dynamic 6G scenarios.