面向低轨卫星系统高效资源分配的拓扑感知协作波束跳变调度
Topology-Aware Cooperative Beam-Hopping Scheduling for Efficient Resource Allocation in LEO Satellite Systems
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- Tiangong University（天津工业大学）
- Beijing University of Posts and Telecommunications（北京邮电大学）
- Beijing Normal-Hong Kong Baptist University Zhuhai（北京师范大学-香港浸会大学联合国际学院）
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中文总结 AI 辅助
针对低轨卫星系统的波束跳变资源管理难题，提出拓扑感知协作波束跳变框架，结合图神经网络与多智能体强化学习实现联合调度与功率控制，仿真显示能效等性能显著优于基线。
中文摘要 AI 辅助
多卫星系统的动态拓扑与异构业务需求给波束跳变（BH）资源管理带来了重大挑战。本文针对部分可观测场景下的多卫星系统，提出一种拓扑感知协作BH框架。首先，轻量级负载均衡方案确定服务关系，利用该关系构建关联诱导的卫星-用户图；接着，两阶段图神经网络（GNN）为参数共享智能体提取回合级拓扑感知结构特征，同时循环多智能体近端策略优化（MAPPO）策略处理时隙级动态，以实现联合波束调度与星上功率控制。仿真结果表明，与基线方案相比，该方案在能效、吞吐量和公平性方面均取得显著提升。
英文摘要
The dynamic topology and heterogeneous traffic demands of multi-satellite systems present substantial challenges for beam-hopping (BH) resource management. This letter develops a topology-aware cooperative BH framework for multi-satellite systems under partial observability. A lightweight load-balancing scheme first determines the serving relationships, which are then exploited to construct an association-induced satellite-user graph. A two-phase graph neural network (GNN) extracts episode-level topology-aware structural identities for parameter-sharing agents, while a recurrent multi-agent proximal policy optimization (MAPPO) policy handles slot-level dynamics for joint beam scheduling and onboard power control. Simulation results demonstrate notable gains in energy efficiency, throughput, and fairness over the baselines.
