Spatio-configurable Resource Management Architecture and Method for Mega Satellite Networks
Mega Satellite Networks(MSNs)are multi-layer satellite networks composed of satellites located at different orbital alti-tudes and with different functions,which can break through the coverage limitations of existing terrestrial networks and achieve high-quality service requirements for future 6G seamless coverage.However,high dynamic movement of various satellites in MSNs results in heterogeneous resources and continuous changes in topology on a large spatio-temporal scale,posing a severe challenge to autonomous management of massive resources and ensuring real-time response to diversified service demands.For this challenge,a spatio-reconfigu-rable resource management architecture is designed for MSNs,and it realizes hierarchical management of heterogeneous resources by de-ploying satellite management nodes with different functional levels in space.Moreover,a multi-dimensional resource scheduling strategy based on Deep Reinforcement Learning(DRL)is proposed to dynamically reconstruct resources of different management nodes for mul-tiple services,which can enhance resource utilization and guarantee service quality.Simulation results verify that the resource manage-ment approach based on the proposed architecture can raise the resource utilization rate by 11.64%and the task completion rate by 46.4%compared with traditional heuristic algorithms.
MSNssoftware defined networkingconfigurable managementDRL
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