基于低轨全覆盖卫星星座的反向缝建链策略

低轨卫星星座的拓扑设计是天地一体化网络部署中较为重要的一环。在低轨卫星网络中，如何设计星座拓扑来提升卫星网络的稳定性与传输性能，是亟待解决的问题。提出一种基于轨道覆盖带方法的低轨持续全覆盖卫星星座设计方法，通过该方法构建低轨卫星星座，满足覆盖性能，可有效减少卫星切换的次数。基于构建的星座拓扑，制定邻轨k最近邻星的反向缝建链策略，优化设计链路长度和链路持续时长，获取网络传输性能与稳定性的平衡。仿真结果表明:通过上述方法设计出的星座，在满足对地持续全覆盖性能的情况下，相较于轨道覆盖带方法切换次数减少10%;邻轨k最近邻星策略相较于最短建链策略，拓扑稳定性能提升79.62%，使网络拓扑稳定性与传输性能达到更优的平衡。     

StarLink星座对空间安全态势的影响

针对StarLink星座部署后带来的空间安全问题，对StarLink星座构型仿真，利用碰撞概率的算法，从碰撞风险的角度分析了StarLink星座部署后对其他卫星在轨安全的影响。结果表明:StarLink星座部署对低轨航天器将带来较大的碰撞风险，碰撞风险比部署前提升一个量级，对535~555 km空间区域的航天器影响尤为突出，一旦产生碰撞碎片将带来更大的碰撞风险。通过分析提出了警示，并针对大型卫星星座带来的碰撞危险提供了应对建议。     

Reinforcement learning based dynamic distributed routing scheme for mega LEO satellite networks

Recently, mega Low Earth Orbit (LEO) Satellite Network (LSN) systems have gained more and more attention due to low latency, broadband communications and global coverage for ground users. One of the primary challenges for LSN systems with inter-satellite links is the routing strategy calculation and maintenance, due to LSN constellation scale and dynamic network topology feature. In order to seek an efficient routing strategy, a Q-learning-based dynamic distributed Routing scheme for LSNs (QRLSN) is proposed in this paper. To achieve low end-to-end delay and low network traffic overhead load in LSNs, QRLSN adopts a multi-objective optimization method to find the optimal next hop for forwarding data packets. Experimental results demonstrate that the proposed scheme can effectively discover the initial routing strategy and provide long-term Quality of Service (QoS) optimization during the routing maintenance process. In addition, comparison results demonstrate that QRLSN is superior to the virtual-topology-based shortest path routing algorithm.