Research on low earth orbit constellation beam hopping resource scheduling based on multi-agent deep reinforcement learning

Abstract

A low earth orbit constellation beam hopping resource scheduling method based on multi-agent deep reinforcement learning was proposed to meet the requirements of low earth orbit constellation beam hopping resource scheduling. The mapping relationship between the satellite and the service area was established by optimizing the access of multi-target satellite selection. On this basis, according to the diversity of service types and QoS requirements, based on the concept of mixture of experts, a resource scheduling multi-agent was constructed to carry out real-time decision scheduling of on-board resources and beam hopping patterns. The simulation results show that compared with the traditional methods, the proposed resource scheduling method can not only meet the performance requirements of different services on delay and throughput, but also effectively balance the algorithm complexity. At the same time, the algorithm can adapt to the converged transmission requirements of diversified services, cope with the uneven spatiotemporal distribution and dynamic changes of traffic and have strong generalization ability.

Keywords

• low earth orbit satellite
• beam hopping
• deep reinforcement learning
• resource scheduling