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Communications and Networking. 18th EAI International Conference, ChinaCom 2023, Sanya, China, November 18–19, 2023, Proceedings

Research Article

Dynamic Resource Allocation for Multi-beam Satellite Communication Systems

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-67162-3_22,
        author={Siya Zhang and Rong Chai and Lei Liu and Guorong Yang},
        title={Dynamic Resource Allocation for Multi-beam Satellite Communication Systems},
        proceedings={Communications and Networking. 18th EAI International Conference, ChinaCom 2023, Sanya, China, November 18--19, 2023, Proceedings},
        proceedings_a={CHINACOM},
        year={2024},
        month={8},
        keywords={Beaming illumination Multi-beam satellite Resource allocation Double deep Q learning},
        doi={10.1007/978-3-031-67162-3_22}
    }
    
  • Siya Zhang
    Rong Chai
    Lei Liu
    Guorong Yang
    Year: 2024
    Dynamic Resource Allocation for Multi-beam Satellite Communication Systems
    CHINACOM
    Springer
    DOI: 10.1007/978-3-031-67162-3_22
Siya Zhang1,*, Rong Chai1, Lei Liu1, Guorong Yang1
  • 1: School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications
*Contact email: s220132218@stu.cqupt.edu.cn

Abstract

Dynamic resource allocation is a crucial concern for achieving flexible and efficient data transmission in satellite communication systems. This paper examines the dynamic resource allocation challenge within a satellite communication system with multiple beams. Considering the difference between user requirement and service providing capability, we establish the concept of system cost and cast the integrated problem of beam illumination, sub-channel, and power allocation as a minimization task for the average system cost. To address the defined issue, we first propose two beam scheduling schemes, then we frame the issue of sub-channel and power allocation as a Markov decision process. We introduce two algorithms, namely, a Double Deep Q Learning (DDQN)-based algorithm and an Improved Priority Experience Replay DDQN (PER-DDQN)-based algorithm, for the joint power and sub-channel allocation. The simulation outcomes illustrate the efficacy and superiority of the proposed algorithms.

Keywords
Beaming illumination Multi-beam satellite Resource allocation Double deep Q learning
Published
2024-08-06
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-67162-3_22
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