Jul 2026· International Conference on Optical Communications and Networks· pp. 1-3· 0 citations· 4 references
Abstract
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 process over a small set of paths. Simulation results demonstrate that, compared with traditional Dijkstra and random routing algorithms, the proposed algorithm fully exploits network resources, effectively prevents network bottlenecks, and significantly enhances the network’s service-carrying capacity.
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...
Multi-agent deep reinforcement learning (MADRL) offers a promising solution for routing in low Earth orbit (LEO) satellite networks. However, large inter-satellite propagation delays lead to severe state information lag in agent interactions, giving rise to decision biases and degraded routing timeliness. To this end,...
Wei-Dan Liu, Tong Liu, Li-Xia Xiao et al.· IEEE Transactions on Cogniti...· 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
To address service function chain routing challenges in satellite-terrestrial integrated networks, an attention-enhanced Direct Reward Policy Optimization(DRPO) method is proposed. Simulations show the proposed method achieves higher utility and admission-rate than Greedy with similar delay.
Xuan Wu, Zikang Li, Qi Zhang et al.· International Conference on...· 0 citations
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