Jul 2026· 2026 6th International Conference on Intelligent Communications and Computing (ICICC)· pp. 181-185· 0 citations· 12 references
Abstract
Low Earth Orbit (LEO) satellite networks face challenging routing conditions due to dynamic topology evolution and uneven spatio-temporal traffic distributions. To address the difficulty of jointly supporting network-wide path adaptation and localized congestion mitigation, this paper proposes STG-SR, a cohesive load-balancing routing framework for softwaredefined satellite networks. Specifically, STG-SR combines a global spatio-temporal graph with deep reinforcement learning for basic path provisioning, while employing a local spatio-temporal graph for forwarding-table reconstruction and congestion-aware multipath traffic splitting. Simulation results under low-density and high-density spatio-temporal traffic patterns show that STG-SR achieves lower delay, reduces the congestion node ratio, and improves traffic distribution compared with Dijkstra, DBPR, and CGR, demonstrating the effectiveness of coordinated global-local routing in dynamic LEO satellite networks.
Low Earth Orbit (LEO) satellites are essential for 6G non-terrestrial networks due to their global coverage and low-latency communication. However, the highly dynamic topology and uneven traffic distribution cause routing inefficiencies. This letter proposes a Graph Transformer–aided Traffic Prediction and Adaptive Rou...
To address load imbalance in low earth orbit (LEO) laser satellite networks (LSN), this paper proposes a deep reinforcement learning (DRL) based routing algorithm, which combines proximal policy optimization (PPO) and K-shortest path (KSP) strategies to transform the large-scale routing problem into a decision-making p...
Haoxin Li, Junling Yuan, Xu-Hong Li et al.· International Conference on...· 0 citations
Routing optimization in cloud-edge collaborative networks faces a fundamental conflict between global strategic planning and local real-time responsiveness, further complicated by structural heterogeneity and stochastic traffic patterns. Traditional protocols lack adaptivity, while existing Deep Reinforcement Learning...
Bin Dai, Yun-Tao Wang, Jianhai Zheng· IEEE Transactions on Network...· 0 citations
Low Earth orbit (LEO) satellite networks exhibit rapidly changing topology and time-varying traffic hotspots, which makes hop-by-hop routing highly sensitive to local congestion and state staleness. Existing routing methods either rely on global path computation or use plain local observations, while graph-enhanced app...
Wen-Xiang Zhang, Yiao Gao, Ke-Yan Bai et al.· 2026 8th International Confe...· 0 citations
In multi-tier low-Earth orbit (LEO) mega-constellations, the mobility of satellites across different orbital altitudes leads to dynamic changes in network topology and inter-satellite link (ISL) states, including ISL duration and capacity. These changes often result in unstable connectivity and disrupted end-to-end dat...
Yoonsoo Choi, Anna Cho, C. Kim et al.· IEEE Wireless Communications...· 0 citations
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