On an Intelligent Hierarchical Routing Strategy for Ultra-Dense Free Space Optical Low Earth Orbit Satellite Networks

As an essential 6G component, the Low Earth Orbit (LEO) satellite communication has aroused increasing attentions from academia and industry to provide seamless and highly-efficient networking services. However, existing routing strategies are primarily designed for terrestrial networks or small-scale satellite networks, making it inapplicable to future LEO satellite constellations of ultra density, high dynamics, and large scale. Moreover, since Free Space Optical (FSO) communications have been expected for Inter-satellite Links (ISLs) and the number of constructed FSO ISLs depends on the Acquisition, Pointing, and Tracking (APT) terminals and geometric visibilities, the routing algorithm needs to be adaptive. To address these issues, this paper considers the dual-layer network architecture composed of Medium Earth Orbit (MEO) satellites and LEO satellites, where the regional network division is adopted for the LEO satellite layer to alleviate the complexity and improve the routing efficiency. Then, a multi-objective reinforcement learning-based routing strategy with local information considered is proposed to meet the differentiated Quality of Service (QoS) requirements of diversified terrestrial applications. A cooperative mechanism is also designed to address the conflicts caused by the routing design for different applications. The simulation results demonstrate the proposal is applicable to varying numbers of APT terminals and outperforms benchmark algorithms in terms of diversified QoS metrics.