← 研踪 PaperTrailarXiv:2605.02413 · v2
Spatial-Temporal Learning-Based Distributed Routing for Dynamic LEO Satellite Networks
首次提交 2026-05-04 18:05
版本更新 2026-11-01 20:26
雷达观测 2026-11-04 23:43
摘要
This paper proposes a spatial-temporal learning-based routing framework for dynamic low Earth orbit (LEO) satellite networks. The routing problem is formulated as a partially observable Markov decision process (POMDP), where each satellite independently selects its next hop using network-state observations and destination information. A graph attention network (GAT) extracts spatial features from the dynamic satellite topology and queue states, while a long short-term memory (LSTM) network captures their temporal evolution. The resulting representation is integrated into a deep Q-network (DQN) for destination-aware next-hop selection without a separate traffic-prediction stage. Simulations in a 45-satellite LEO constellation under normal and bursty traffic demonstrate packet delivery performance comparable to a graph neural network (GNN)-enhanced routing baseline, although the proposed method incurs higher delay and queue occupancy. Ablation studies further demonstrate the complementary benefits of spatial and temporal learning.
作者备注：6 pages, 4 figures, 2 tables. Submitted to the IEEE International Conference on Communications (ICC) 2027
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v2 · 2026-11-01 20:26 · 最新观测版本
Spatial-Temporal Learning-Based Distributed Routing for Dynamic LEO Satellite Networks
This paper proposes a spatial-temporal learning-based routing framework for dynamic low Earth orbit (LEO) satellite networks. The routing problem is formulated as a partially observable Markov decision process (POMDP), where each satellite independently selects its next hop using network-state observations and destination information. A graph attention network (GAT) extracts spatial features from the dynamic satellite topology and queue states, while a long short-term memory (LSTM) network captures their temporal evolution. The resulting representation is integrated into a deep Q-network (DQN) for destination-aware next-hop selection without a separate traffic-prediction stage. Simulations in a 45-satellite LEO constellation under normal and bursty traffic demonstrate packet delivery performance comparable to a graph neural network (GNN)-enhanced routing baseline, although the proposed method incurs higher delay and queue occupancy. Ablation studies further demonstrate the complementary benefits of spatial and temporal learning.
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