Satellite networks provide communication services to global users with an uneven geographical distribution. In densely populated regions, Inter-satellite links (ISLs) often experience congestion, blocking traffic from other links and leading to low link utilization and throughput. In such cases, delay-tolerant traffic can be withheld by moving satellites and carried to navigate congested areas, thereby mitigating link congestion in densely populated regions. Through rational store-and-forward decision-making, link utilization and throughput can be improved. Building on this foundation, this letter centers its focus on learning-based decision-making for satellite traffic. First, a link load prediction method based on topology isomorphism is proposed. Then, a Markov decision process (MDP) is formulated to model store-and-forward decision-making. To generate store-and-forward policies, we propose reinforcement learning algorithms based on value iteration and Q-Learning. Simulation results demonstrate that the proposed method improves throughput and link utilization while consuming less than 20$\%$ of the time required by constraint-based routing.

翻译：卫星网络为全球用户提供通信服务，但用户地理分布不均。在人口稠密地区，星间链路（ISL）常发生拥塞，阻塞其他链路的流量，导致链路利用率和吞吐量低下。在此类场景中，可通过移动卫星暂存时延容忍型业务，并将其携带至拥塞区域外传输，从而缓解人口密集区域的链路拥塞。通过合理的存储转发决策，链路利用率和吞吐量可得到提升。基于此，本文聚焦于基于学习的卫星业务决策方法。首先，提出一种基于拓扑同构的链路负载预测方法。其次，建立马尔可夫决策过程（MDP）以建模存储转发决策机制。为生成存储转发策略，我们提出了基于值迭代与Q-Learning的强化学习算法。仿真结果表明，所提方法在提升吞吐量与链路利用率的同时，耗时不足基于约束路由方案所需时间的20%。