基于强化学习的卫星网络资源调度机制

与传统同步轨道通信卫星(GEO)相比,以SpaceX、Starlink、O3b等为代表的新一代中低轨卫星互联网星座具备广域覆盖、全时空互联、多星协同等显著优势,已成为当今世界各国研究的焦点之一。传统卫星资源调度方法主要研究单颗GEO卫星下的资源调度问题,难以满足以多星协同、联合组网、海量用户为特征的低轨卫星星座的资源调度需求。为此,构建了基于用户满意度的多星协同智能资源调度模型,提出了一种基于强化学习的卫星网络资源调度机制IRSUP。IRSUP针对用户服务定制的个性化需求,设计了用户服务偏好智能优化模块;针对多星资源联合优化难题,设计了基于强化学习的智能调度模块。模拟仿真结果表明:IRSUP能有效提高资源调度合理性、链路资源利用率和用户满意度等指标,其中业务容量提升30%～60%,用户满意度提升一倍以上。

A satellite network resource scheduling mechanism based on reinforcement learning

Compared with the traditional geostationary earth orbit (GEO) satellite, the new generation of medium-low-orbit satellite Internet constellation represented by SpaceX, Starlink and O3b has significant advantages such as wide-area coverage, full-time interconnection and multi-star coordination, and has become one of the research focuses in the world today. The traditional satellite resource scheduling method mainly studies the resource scheduling problems with single GEO satellite, which is difficult to meet the resource scheduling requirements of the low-orbit satellite constellation characterized by multi-satellite coordination, joint networking, and mass users. Consequently, an intelligent multi-star collaborative resource scheduling model based on user satisfaction is constructed, and a satellite network resource scheduling mechanism named IRSUP based on reinforcement learning is proposed. IRSUP designs an intelligent user service preference optimization module for the personalized needs of user service customization and an intelligent scheduling module based on reinforcement learning for the problems with joint optimization of multi-star resources. The simulation results show that IRSUP can effectively improve the rationality of resource scheduling, link resource utilization and user satisfaction, among which the business capacity is increased by 30%~60%, and user satisfaction is increased by more than twice.