Space-Integrated-Ground Information Networks ›› 2021, Vol. 2 ›› Issue (4): 67-74.doi: 10.11959/j.issn.2096-8930.2021045

Special Issue: 专题:卫星互联网运行控制与管理

• Special Issue: Satellite Internet Operation Control and Management • Previous Articles     Next Articles

Research on Strategies and Technologies for Resource Management and Control of Heterogeneous Network of High and Low Orbit Satellites

Meirong ZHANG1, Mengting HAO1, Chuang WANG2, Gengxin ZHANG1   

  1. 1 College of Telecommunications&Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
    2 A Certain Department of PLA, 212000, China
  • Revised:2021-10-21 Online:2021-12-20 Published:2021-12-01
  • Supported by:
    The National Natural Science Foundation of China(91738201)

Abstract:

With the vigorous development of space communication technology and the continuous advancement of space-integrated-ground information network, satellite communication networks resource management and control is becoming more and more complex.Due to the scarcity of satellite resources, the slowness of resource scheduling compared to the status refreshing, and uneven distribution of business, eff ciently managing resources has become one of urgent problems to be solved in the development of satellite communications.In view of the heterogeneous network system architecture of high and low orbit satellites, the challenges, its network resource management and control facing, were analyzed.Integrating on the basis of traditional management and control architecture, the collaborative management and control architecture based on group management was introduced.The management strategy of satellite network virtual resource pool was explained to relieved resources scarcity.Resource scheduling algorithms based on deep reinforcement learning(DRL) was introduced to solved the mismatch problem of traditional scheduling methods in complex environments.Beam-hopping technology was adopted to deal with the two-dimensional unevenness of service distribution in time and space.

Key words: heterogeneous network, resource management and control, virtual resource pool, deep reinforcement learning, beam-hopping technology

CLC Number: 

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