首页|Multi-QoS Routing Algorithm Based on Reinforcement Learning for LEO Satellite Networks
Multi-QoS Routing Algorithm Based on Reinforcement Learning for LEO Satellite Networks
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NETL
NSTL
Systems Engineering & Electronics, Editorial Dept
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.
SatellitesRoutingQuality of serviceDelaysReactive powerLow earth orbit satellitesHeuristic algorithmsReinforcement learningPlanetary orbitsEarth
Yifan Zhang、Tao Dong、Zhihui Liu、Shichao Jin
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School of Integrated Circuit and Electronics, Beijing Institute of Technology, Beijing, China|State Key Laboratory of Space-Ground Integrated Information Technology, Space Star Technology Co., Ltd., Beijing, China
State Key Laboratory of Space-Ground Integrated Information Technology, Space Star Technology Co., Ltd., Beijing, China
NETL
NSTL
Systems Engineering & Electronics, Editorial Dept
