Flexible beam scheduling and resource allocation strategies for satellite Internet of things
Satellite communication systems based on multi-beam technology can provide strong support for massive machine type communication application scenarios of 5th generation mobile communication technology as well as for next-generation communication visions, such as access and transmission of massive In...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
China InfoCom Media Group
2025-03-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2025.00465/ |
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| Summary: | Satellite communication systems based on multi-beam technology can provide strong support for massive machine type communication application scenarios of 5th generation mobile communication technology as well as for next-generation communication visions, such as access and transmission of massive Internet of things (IoT) devices and ubiquitous communication. In the satellite IoT-oriented massive terminal application scenario, the traffic distribution of IoT terminals is non-uniform. Further, improving the communication resource utilization efficiency of multi-beam satellite systems has become an important research direction. Multi-beam scheduling and wireless resource allocation are the key issues to improve the resource utilization and fairness of the system. Firstly, The coupling between beam scheduling and wireless resource allocation was analyzed. Subsequently, a joint optimization strategy for flexible beam scheduling and resource allocation was proposed. The beam scheduling algorithm based on separated swarm optimization (SSO-BSA) was proposed to solve the flexible beam pointing coordinates, and an on-demand resource allocation algorithm based on service value degree (ORAA-SVD) was designed to provide flexible resource allocation for beams and IoT terminals. The simulation verifies the performance of the proposed algorithm and the benchmark algorithms under different traffic intensities for each metric. The simulation results show that the proposed algorithm has better performance than that of the benchmark algorithms in terms of fairness and resource utilization. |
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| ISSN: | 2096-3750 |