LEO satellite Internet resource allocation strategy based on terminal traffic prediction

SHEN Fei; LYU Chengcheng; ZHANG Jiaxuan; RUAN Xiaoting

doi:10.11959/j.issn.1000-436x.2024049

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LEO satellite Internet resource allocation strategy based on terminal traffic prediction

Topics：Low Earth Orbit Satellite Internet | 更新时间：2024-08-09

- LEO satellite Internet resource allocation strategy based on terminal traffic prediction
- Journal on Communications Vol. 45, Issue 7, Pages: 21-36(2024)
- 作者机构：
  
  1.中国科学院上海微系统与信息技术研究所无线传感网与通信重点实验室，上海 200050
  2.中国科学院大学，北京 101408
- 作者简介：
- 基金信息：
  
  The Project of Science and Technology Commission of Shanghai(22511100800;22511100500)
- DOI：10.11959/j.issn.1000-436x.2024049
  CLC： TN927
- Received：08 November 2023，
  
  Revised：2024-01-17，
  
  Published：25 July 2024
- 稿件说明：
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沈斐,吕承丞,张嘉璇等.基于终端流量预测的低地球轨道卫星互联网资源分配策略[J].通信学报,2024,45(07):21-36.

SHEN Fei,LYU Chengcheng,ZHANG Jiaxuan,et al.LEO satellite Internet resource allocation strategy based on terminal traffic prediction[J].Journal on Communications,2024,45(07):21-36.
沈斐,吕承丞,张嘉璇等.基于终端流量预测的低地球轨道卫星互联网资源分配策略[J].通信学报,2024,45(07):21-36. DOI： 10.11959/j.issn.1000-436x.2024049.

SHEN Fei,LYU Chengcheng,ZHANG Jiaxuan,et al.LEO satellite Internet resource allocation strategy based on terminal traffic prediction[J].Journal on Communications,2024,45(07):21-36. DOI： 10.11959/j.issn.1000-436x.2024049.

摘要

针对地面网络存在覆盖盲区和卫星网络通信资源利用率低等问题，提出了基于终端流量预测的低地球轨道（LEO）卫星互联网资源分配策略。该策略利用真实数据集提出改进LSTM-ARIMA算法，准确预测地面区域未来一定时间内产生的数据流量，通过Stackelberg博弈构建差分化数据传输和任务卸载2种通信模型。综合考虑数据处理时延和能耗，通过求解纳什均衡，得到用户通过LEO卫星互联网传输数据或卸载任务的最优比率，以及卫星提供网络服务的最优定价。仿真结果表明，所提策略在数据传输服务中收益能提高约40%，在任务卸载服务中收益能提高约50%。

Abstract

A resource allocation strategy for the low earth orbit (LEO) satellite Internet based on terminal traffic prediction was proposed to address the problems of blind coverage spots in ground network and the low resource utilization of satellite network. An improved LSTM-ARIMA algorithm was proposed with real datasets by the strategy to accurately predict the data traffic generated in the ground area over a certain period of time in the future. Two communication models

differentiated data transmission and task offloading were constructed through Stackelberg games

taking into the data processing latency and energy consumption account. By solving the Nash equilibrium

the optimal ratio for users to transmit data or unload tasks through the LEO satellite Internet

as well as the optimal pricing for satellites to provide network services

were obtained. Extensive simulation results verify that the proposed strategy can increase the revenue by approximately 40% in data transmission services and 50% in task offloading services.

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references

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