The integration of Software-Defined Networking (SDN) and Artificial Intelligence (AI) presents promising opportunities for managing and optimizing LEO satellite network routing. However, as the scale and coverage of satellite networks continue to expand, challenges are posed to both centralized and distributed architectures in terms of managing network information and coping with routing complexity. To overcome these challenges, leveraging distributed SDN technology, a stigmergy multi-agent hierarchical deep reinforcement learning routing algorithm is proposed in multi-domain collaborative satellite networks. A pheromone-based mechanism is incorporated to facilitate collaboration during independent training, and hierarchical control is employed to decouple the complexity of cross-domain routing decisions. Simulation results demonstrate that our proposed algorithm exhibits good scalability and performance in large-scale satellite networks.

中文翻译：

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多域协作卫星网络中路由优化的 Stigmergy 和分层学习

软件定义网络 (SDN) 和人工智能 (AI) 的集成为管理和优化 LEO 卫星网络路由提供了广阔的前景。然而，随着卫星网络规模和覆盖范围的不断扩大，集中式和分布式架构在管理网络信息和应对路由复杂性方面都提出了挑战。为了克服这些挑战，利用分布式SDN技术，在多域协作卫星网络中提出了一种stigmergy多智能体分层深度强化学习路由算法。采用基于信息素的机制来促进独立训练期间的协作，并采用分层控制来解耦跨域路由决策的复杂性。仿真结果表明，我们提出的算法在大规模卫星网络中表现出良好的可扩展性和性能。