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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.16184 |
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| _version_ | 1866915456827260928 |
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| author | Zheng, Yuhao You, Ting Peng, Kejia Liu, Chang |
| author_facet | Zheng, Yuhao You, Ting Peng, Kejia Liu, Chang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16184 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Cache Placement and Routing in Satellite-Terrestrial Edge Computing Network: A GNN-Enabled DRL Approach Zheng, Yuhao You, Ting Peng, Kejia Liu, Chang Networking and Internet Architecture 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. |
| title | Joint Cache Placement and Routing in Satellite-Terrestrial Edge Computing Network: A GNN-Enabled DRL Approach |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2508.16184 |