Joint Cache Placement and Routing in Satellite-Terrestrial Edge Computing Network: A GNN-Enabled DRL Approach

TL;DR

This paper presents a novel GNN-enabled DRL framework for joint cache placement and routing in satellite-terrestrial networks, effectively handling dynamic topologies and content demands to enhance service quality.

Contribution

It introduces a learning-based approach combining GNNs and DRL for joint caching and routing in dynamic satellite-terrestrial networks, addressing topology and demand heterogeneity.

Findings

Improves delivery success rate significantly

Reduces communication traffic cost

Handles dynamic satellite topologies effectively

Abstract

In this letter, we investigate the problem of joint content caching and routing in satellite-terrestrial edge computing networks (STECNs) to improve caching service for geographically distributed users. To handle the challenges arising from dynamic low Earth orbit (LEO) satellite topologies and heterogeneous content demands, we propose a learning-based framework that integrates graph neural networks (GNNs) with deep reinforcement learning (DRL). The satellite network is represented as a dynamic graph, where GNNs are embedded within the DRL agent to capture spatial and topological dependencies and support routing-aware decision-making. The caching strategy is optimized by formulating the problem as a Markov decision process (MDP) and applying soft actor-critic (SAC) algorithm. Simulation results demonstrate that our approach significantly improves the delivery success rate and reduces communication traffic cost.