Multi-QoS routing algorithm based on reinforcement learning for LEO satellite networks  

作　　者：ZHANG Yifan DONG Tao LIU Zhihui JIN Shichao 

机构地区：[1]School of Integrated Circuit and Electronics,Beijing Institute of Technology,Beijing 100081,China [2]State Key Laboratory of Space-Ground Integrated Information Technology,Space Star Technology Co.,Ltd.,Beijing 100095,China

出　　处：《Journal of Systems Engineering and Electronics》2025年第1期37-47,共11页系统工程与电子技术(英文版)

基　　金：National Key Research and Development Program(2021YFB2900604)。

摘　　要：Low Earth orbit(LEO)satellite networks exhibit distinct characteristics,e.g.,limited resources of individual satellite nodes and dynamic network topology,which have brought many challenges for routing algorithms.To satisfy quality of service(QoS)requirements of various users,it is critical to research efficient routing strategies to fully utilize satellite resources.This paper proposes a multi-QoS information optimized routing algorithm based on reinforcement learning for LEO satellite networks,which guarantees high level assurance demand services to be prioritized under limited satellite resources while considering the load balancing performance of the satellite networks for low level assurance demand services to ensure the full and effective utilization of satellite resources.An auxiliary path search algorithm is proposed to accelerate the convergence of satellite routing algorithm.Simulation results show that the generated routing strategy can timely process and fully meet the QoS demands of high assurance services while effectively improving the load balancing performance of the link.

关 键 词：low Earth orbit(LEO)satellite network reinforcement learning multi-quality of service(QoS) routing algorithm 

分 类 号：TN927.2[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]