Efficient satellite beam scheduling improves resources and energy use
Topology-Aware Cooperative Beam-Hopping Scheduling for Efficient Resource Allocation in LEO Satellite Systems
Networking and Internet Architecture
Summary
Managing signals between many low Earth orbit satellites and users is tricky because satellite positions and user needs constantly change. The authors developed a new method that helps satellites share their load and decide when and where to send signals more efficiently. They use a kind of AI called graph neural networks to learn how satellites connect with users over time. This approach improves overall data speed, fairness, and uses energy better compared to older methods.
What this means in practice
- •For satellite network operators: Improve signal scheduling and power use for fleets of low Earth orbit satellites serving users with varying data needs.
- •For wireless communication engineers: Design adaptive beam scheduling algorithms using cooperative multi-agent learning to handle dynamic satellite and user environments.
Authors
Shuang Zheng, Xing Zhang, Quan Z. Sheng, Wenbo Wang
Abstract
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.