Graph Learning Based Transmission Resources Scheduling in Dense Space Networks

被引：0

作者：

Liu R.-Z. ^{[1
]}

Wu W.-H. ^{[2
]}

Zhang W.-Z. ^{[1
]}

Zhou D. ^{[2
]}

Zhang Y. ^{[2
]}

机构：

[1] School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an

[2] The State Key Laboratory of ISN, Xidian University, Xi'an

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2021年 / 49卷 / 11期

关键词：

Deep reinforcement learning; Dense space network; Graph theory; Machine learning; Resource scheduling;

D O I：

10.12263/DZXB.20201217

中图分类号：

学科分类号：

摘要：

Facing the challenges of the transmission resource schedule of dense space networks, we combine the mathematical models and machine learning methods, and propose a graph learning based approach for the scheduling of transmission resources in dense space networks. In the proposed method, transmission resource scheduling problem is decomposed based on the knowledge of the problem structure brought by graph theory. On this basis, mathematical model and reinforcement learning alternately complete the whole solution process. Simulation results show that, compared with the traditional mathematical model-based methods, the proposed method improves the scheduling profits by 25.1%, and its training results have better generality. © 2021, Chinese Institute of Electronics. All right reserved.

引用

页码：2133 / 2137

页数：4

共 13 条

[1] Zhang Z, Zhang W, Tseng F H., Satellite mobile edge computing: Improving QoS of high-speed satellite-terrestrial networks using edge computing techniques, IEEE Network, 33, 1, pp. 70-76, (2019)
[2] Jiang H L, Fu Q, Zhao Y W, LIU X Z., Development status and trend of space information network and laser communication, Chinese Journal on Internet of Things, 3, 2, pp. 1-8, (2019)
[3] Rojanasoonthon S, Bard J., A GRASP for parallel machine scheduling with time windows, INFORMS Journal on Computing, 17, 1, pp. 32-51, (2005)
[4] Wang L, Jiang C, Kuang L, Et al., Mission scheduling in space network with antenna dynamic setup times, IEEE Transactions on Aerospace and Electronic Systems, 55, 1, pp. 31-45, (2019)
[5] Baek S, Han S, Cho K, Et al., Development of a scheduling algorithm and GUI for autonomous satellite missions, Acta Astronautica, 68, 7-8, pp. 1396-1402, (2011)
[6] Barbulescu L, Watson J P, Whitley L D, Et al., Scheduling space-ground communications for the air force satellite control network, Journal of Scheduling, 7, 1, pp. 7-34, (2004)
[7] Chen M, Challita U, Saad W, Et al., Artificial neural networks-based machine learning for wireless networks: A tutorial, IEEE Communications Surveys & Tutorials, 21, 4, pp. 3039-3071, (2019)
[8] Liang L, Ye H, Yu G, Et al., Deep-learning-based wireless resource allocation with application to vehicular networks, Proceedings of the IEEE, 108, 2, pp. 341-356, (2019)
[9] Sun H, Chen X, Shi Q, Et al., Learning to optimize: Training deep neural networks for interference management, IEEE Transactions on Signal Processing, 66, 20, pp. 5438-5453, (2018)
[10] Deng B, Jiang C, Yao H, Et al., The next generation heterogeneous satellite communication networks: Integration of resource management and deep reinforcement learning, IEEE Wireless Communications, 27, 2, pp. 105-111, (2019)

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