AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (11.5 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Data-Driven Collaborative Scheduling Method for Multi-Satellite Data-Transmission

School of Computer Science, China University of Geosciences, Wuhan 430074, China
School of Electronic Engineering, Xidian University, Xi’an 710071, China
School of Computer Science, Shaanxi Normal University, Taiyuan 710119, China
Show Author Information

Abstract

With continuous expansion of satellite applications, the requirements for satellite communication services, such as communication delay, transmission bandwidth, transmission power consumption, and communication coverage, are becoming higher. This paper first presents an overview of the current development status of Low Earth Orbit (LEO) satellite constellations, and then conducts a demand analysis for multi-satellite data transmission based on LEO satellite constellations. The problem is described, and the challenges and difficulties of the problem are analyzed accordingly. On this basis, a multi-satellite data-transmission mathematical model is then constructed. Combining classical heuristic allocating strategies on the features of the proposed model, with the reinforcement learning algorithm Deep Q-Network (DQN), a two-stage optimization framework based on heuristic and DQN is proposed. Finally, by taking into account the spatial and temporal distribution characteristics of satellite and facility resources, a multi-satellite scheduling instance dataset is generated. Experimental results validate the rationality and correctness of the DQN algorithm in solving the collaborative scheduling problem of multi-satellite data transmission.

Keywords

schedulingGenetic Algorithm (GA)data transmissionrelay satelliteDeep Q-Network (DQN)

References

[1]
Be prepared, stay connected, https://www.iridium.com/, 2020.
[2]

X. Lin, S. Cioni, G. Charbit, N. Chuberre, S. Hellsten, and J. F. Boutillon, On the path to 6G: Embracing the next wave of low earth orbit satellite access, IEEE Commun. Mag., vol. 59, no. 12, pp. 36–42, 2021.
Crossref Google Scholar
[3]

F. S. Prol, R. M. Ferre, Z. Saleem, P. Välisuo, C. Pinell, E. S. Lohan, M. Elsanhoury, M. Elmusrati, S. Islam, K. Çelikbilek, et al, Position, navigation, and timing (PNT) through low earth orbit (LEO) satellites: A survey on current status, challenges, and opportunities, IEEE Access, vol. 10, pp. 83971–84002, 2022.
Crossref Google Scholar
[4]

S. Chen, Analysis of LEO satellite communication and suggestions for its development strategy in China, (in Chinese), Telecommun. Sci., vol. 36, no. 6, pp. 1–13, 2020.
Google Scholar
[5]

H. Zhang, J. Xie, J. Ge, J. Shi, and Z. Zhang, Hybrid particle swarm optimization algorithm based on entropy theory for solving DAR scheduling problem, Tsinghua Science and Technology, vol. 24, no. 3, pp. 282–290, 2019.
Crossref Google Scholar
[6]

Y. Fu, Y. Hou, Z. Wang, X. Wu, K. Gao, and L. Wang, Distributed scheduling problems in intelligent manufacturing systems, Tsinghua Science and Technology, vol. 26, no. 5, pp. 625–645, 2021.
Crossref Google Scholar
[7]

Z. Hu and D. Li, Improved heuristic job scheduling method to enhance throughput for big data analytics, Tsinghua Science and Technology, vol. 27, no. 2, pp. 344–357, 2022.
Crossref Google Scholar
[8]

E. Jiang, L. Wang, and J. Wang, Decomposition-based multi-objective optimization for energy-aware distributed hybrid flow shop scheduling with multiprocessor tasks, Tsinghua Science and Technology, vol. 26, no. 5, pp. 646–663, 2021.
Crossref Google Scholar
[9]
Y. Yan, K. Du, L. Wang, H. Niu, and X. Wen, MP-DQN based task scheduling for RAN QoS fluctuation minimizing in public clouds, in Proc. 2022 IEEE Int. Conf. Communications Workshops (ICC Workshops ), Seoul, Republic of Korea, 2022, pp. 878–884.
Crossref
[10]

G. Chen, R. Shao, F. Shen, and Q. Zeng, Slicing resource allocation based on dueling DQN for eMBB and URLLC hybrid services in heterogeneous integrated networks, Sensors, vol. 23, no. 5, p. 2518, 2023.
Crossref Google Scholar
[11]

M. Wang, G. Dai, and M. Vasile, Heuristic scheduling algorithm oriented dynamic tasks for imaging satellites, Math. Probl. Eng., vol. 2014, p. 234928, 2014.
Crossref Google Scholar
[12]

X. Chen, G. Reinelt, G. Dai, and M. Wang, Priority-based and conflict-avoidance heuristics for multi-satellite scheduling, Appl. Soft Comput., vol. 69, pp. 177–191, 2018.
Crossref Google Scholar
[13]

X. Chen, G. Reinelt, G. Dai, and A. Spitz, A mixed integer linear programming model for multi-satellite scheduling, Eur. J. Oper. Res., vol. 275, no. 2, pp. 694–707, 2019.
Crossref Google Scholar
[14]

S. Huang, D. Ma, D. Fang, and T. Cui, Satellite data transmission scheduling based on improved ant colony system, (in Chinese), Radio Eng., vol. 45, no. 7, pp. 27–30 & 58, 2015.
Google Scholar
[15]
J. Yan, Research on dynamic scheduling approach of relay satellites, (in Chinese), Master dissertation, Xidian University, Xi’an, China, 2017.
[16]

Y. Xiang, W. Zhang, and M. Tian, Satellite data transmission integrated scheduling and optimization, (in Chinese), Syst. Eng. Electron., vol. 40, no. 6, pp. 1288–1293, 2018.
Google Scholar
[17]
H. Fan, Research on resource scheduling of ground station based on satellite data transmission and telemetry tracing and control tasks, (in Chinese), Master dissertation, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China, 2021.
[18]
G. Dong, Research on satellite network data transmission and task scheduling, (in Chinese), Master dissertation, Guilin University of Electronic Technology, Guilin, China, 2022.
[19]
Y. Du, Research on the general-purpose scheduling engine for satellite task scheduling problems, (in Chinese), PhD dissertation, National University of Defense Technology, Changsha, China, 2021.
[20]
H. Wang, Massive scheduling method under online situation for satellites based on reinforcement learning, (in Chinese), PhD dissertation, University of Chinese Academy of Sciences, Beijing, China, 2018.
[21]
M. Chen, Y. Chen, Y. Chen, and W. Qi, Deep reinforcement learning for agile satellite scheduling problem, in Proc. 2019 IEEE Symp. Series on Computational Intelligence (SSCI ), Xiamen, China, 2019, pp. 126–132.
Crossref
[22]
X. Bao, S. Zhang, and X. Zhang, An effective method for satellite mission scheduling based on reinforcement learning, in Proc. 2020 Chinese Automation Congress (CAC ), Shanghai, China, 2020, pp. 4037–4042.
Crossref
[23]

Y. Liu, Q. Chen, C. Li, and F. Wang, Mission planning for earth observation satellite with competitive learning strategy, Aerosp. Sci. Technol., vol. 118, p. 107047, 2021.
Crossref Google Scholar
[24]

L. Ren, X. Ning, and J. Li, Hierarchical reinforcement-learning for real-time scheduling of agile satellites, IEEE Access, vol. 8, pp. 220523–220532, 2020.
Crossref Google Scholar
[25]

Y. He, L. Xing, Y. Chen, W. Pedrycz, L. Wang, and G. Wu, A generic Markov decision process model and reinforcement learning method for scheduling agile earth observation satellites, IEEE Trans. Syst. Man Cybern. Syst., vol. 52, no. 3, pp. 1463–1474, 2022.
Crossref Google Scholar
[26]

M. Zuo, G. Dai, L. Peng, and M. Wang, An envelope curve-based theory for the satellite coverage problems, Aerosp. Sci. Technol., vol. 100, p. 105750, 2020.
Crossref Google Scholar
[27]

X. Chen, Z. Xu, and L. Shang, Satellite internet of things: Challenges, solutions, and development trends, Front. Inf. Technol. Electron. Eng., vol. 24, no. 7, pp. 935–945, 2023.
Crossref Google Scholar
[28]

Z. Zhang, Y. Bai, and C. Li, Overview of developments in global navigation satellite systems, (in Chinese), Sci. Technol. Innovation, no. 9, pp. 150–152, 2023.
Google Scholar
[29]

W. Sun, G. Yang, C. Chen, M. Chang, K. Huang, X. Meng, and L. Liu, Development status and literature analysis of China’s earth observation remote sensing satellites, (in Chinese), Natl. Remote Sens. Bull., vol. 24, no. 5, pp. 479–510, 2020.
Crossref Google Scholar
[30]

C. Fan, X. Chen, A. Liu, and J. Luo, Read the “sky chain”: A text to understand China’s “data relay satellite corps”, (in Chinese), Space Int., no. 8, pp. 7–10, 2022.
Google Scholar
[31]

Z. Zhang, D. Wang, X. Man, and Y. Zhang, Application prospect and challenge of low orbit internet system in navigation enhancement service, (in Chinese), World Sci-Tech R&D, vol. 45, no. 3, pp. 266–275, 2023.
Google Scholar
[32]

X. Zhang and F. Ma, Review of the development of LEO navigation-augmented GNSS, (in Chinese), Acta Geod. Cartogr. Sin., vol. 48, no. 9, pp. 1073–1087, 2019.
Google Scholar
[33]

Y. Meng, L. Bian, Y. Wang, T. Yan, W. Lei, M. He, and X. Li, Global navigation augmentation system based on Hongyan satellite constellation, (in Chinese), Space Int., no. 10, pp. 20–27, 2018.
Crossref Google Scholar
[34]

X. Wu, D. Qin, Y. Zhang, and Y. Li, Supporting capability for custody layer of the next-generation space architecture, (in Chinese), J. Inf. Eng. Univ., vol. 24, no. 3, pp. 379–384, 2023.
Google Scholar
[35]

P. Zhao and Z. Chen, An adapted genetic algorithm applied to satellite autonomous task scheduling, (in Chinese), Chin. Space Sci. Technol., vol. 36, no. 6, pp. 47–54, 2016.
Google Scholar
Tsinghua Science and Technology
Volume 29 Issue 5,
October 2024
Pages 1463-1480
DOI: 10.26599/TST.2023.9010131
Cite this article:
Chen X, Gu W, Dai G, et al. Data-Driven Collaborative Scheduling Method for Multi-Satellite Data-Transmission. Tsinghua Science and Technology, 2024, 29(5): 1463-1480. https://doi.org/10.26599/TST.2023.9010131

350

Views

42

Downloads

1

Crossref

0

Web of Science

1

Scopus

0

CSCD

Altmetrics
Received: 13 July 2023
Revised: 11 October 2023
Accepted: 25 October 2023
Published: 02 May 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return