The rapid growth of space-based services has established LEO satellite networks as a promising option for global broadband connectivity. Next-generation LEO networks leverage inter-satellite links (ISLs) to provide faster and more reliable communications compared to traditional bent-pipe architectures, even in remote regions. However, the high mobility of satellites, dynamic traffic patterns, and potential link failures pose significant challenges for efficient and resilient routing. To address these challenges, we model the LEO satellite network as a time-varying graph comprising a constellation of satellites and ground stations. Our objective is to minimize a weighted sum of average delay and packet drop rate. Each satellite independently decides how to distribute its incoming traffic to neighboring nodes in real time. Given the infeasibility of finding optimal solutions at scale, due to the exponential growth of routing options and uncertainties in link capacities, we propose SKYLINK, a novel fully distributed learning strategy for link management in LEO satellite networks. SKYLINK enables each satellite to adapt to the time-varying network conditions, ensuring real-time responsiveness, scalability to millions of users, and resilience to network failures, while maintaining low communication overhead and computational complexity. To support the evaluation of SKYLINK at global scale, we develop a new simulator for large-scale LEO satellite networks. For 25.4 million users, SKYLINK reduces the weighted sum of average delay and drop rate by 29% compared to the bent-pipe approach, and by 92% compared to Dijkstra. It lowers drop rates by 95% relative to k-shortest paths, 99% relative to Dijkstra, and 74% compared to the bent-pipe baseline, while achieving up to 46% higher throughput. At the same time, SKYLINK maintains constant computational complexity with respect to constellation size.

翻译：空间业务的快速增长使低轨卫星网络成为全球宽带连接的有前景方案。与传统弯管架构相比，新一代低轨网络利用星间链路在偏远地区也能提供更快、更可靠的通信。然而，卫星的高机动性、动态流量模式以及潜在的链路故障，给高效且弹性的路由带来了重大挑战。为解决这些问题，我们将低轨卫星网络建模为一个由卫星星座和地面站构成的时变图。目标是最小化平均时延与丢包率的加权和。每颗卫星独立决策如何将自身接入流量实时分配至邻近节点。由于路由选项的指数级增长和链路容量的不确定性，在大规模场景下寻找最优解不可行。为此，我们提出SKYLINK——一种全新的低轨卫星网络链路管理全分布式学习策略。该策略使每颗卫星能够适应时变的网络条件，确保实时响应能力、对数百万用户的可扩展性，以及面对网络故障的弹性，同时保持较低通信开销和计算复杂度。为在全局尺度下评估SKYLINK，我们开发了一种大规模低轨卫星网络仿真器。针对2540万用户，SKYLINK使平均时延与丢包率的加权和相比弯管方案降低29%，相比Dijkstra算法降低92%；其丢包率相比k最短路径算法降低95%，相比Dijkstra算法降低99%，相比弯管基线降低74%，同时吞吐量最高提升46%。此外，SKYLINK的计算复杂度与星座规模无关。