Joint Optimization Communication and Computing Resource for LEO Satellites with Edge Computing

JIA Min; WU Jian; ZHANG Liang; GUO Qing

doi:10.23919/cje.2022.00.314

Volume 32 Issue 5

Sep. 2023

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Article Navigation > Chinese Journal of Electronics > 2023 > 32(5): 1011-1021

JIA Min, WU Jian, ZHANG Liang, et al., “Joint Optimization Communication and Computing Resource for LEO Satellites with Edge Computing,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 1011-1021, 2023, doi: 10.23919/cje.2022.00.314

Citation:

JIA Min, WU Jian, ZHANG Liang, et al., “Joint Optimization Communication and Computing Resource for LEO Satellites with Edge Computing,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 1011-1021, 2023, doi: 10.23919/cje.2022.00.314

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Joint Optimization Communication and Computing Resource for LEO Satellites with Edge Computing

doi: 10.23919/cje.2022.00.314

1.
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150000, China

Funds: This work is supported by the National Natural Science Foundation of China (62231012) and the Natural Science Foundation for Outstanding Young Scholars of Heilongjiang Province (YQ2020F001)

More Information

Author Bio:
Min JIA received the M.S. degree in information and communication engineering from Harbin Institute of Technology (HIT), China, in 2006, and the Ph.D. degree from SungKyungKwan University of Korea and Harbin Institute of Technology in 2010. She is currently a Professor and Ph.D. Supervisor in the School of Electronic and Information Engineering, HIT. Her research interests focus on advanced mobile communication technology for LTE and 5G, cognitive radios, digital signal processing and advanced broadband satellite communication systems. She is the General Chair of the IEEE GLOBECOM 2019 Workshop on Intelligent and Cognitive Space, Terrestrial and Ocean Internet, Systems and Applications (ICSTO). She is also a member of the Steering Committee of the WiSATs international conference. She has won six best paper awards at several international conferences. (Email: jiamin@hit.edu.cn)

Jian WU received the B.S. degree from Hainan University (HNU) in 2020. He is currently a Ph.D. student at the Communication Research Center and School of Electronics and Information Engineering, Harbin Institute of Technology. His research interests include mobile edge computing and satellite edge computing. (Email: 20B905014@stu.hit.edu.cn)

Liang ZHANG received the M.S. degree from Ningxia University in 2020. He is currently a Ph.D. student at the Communication Research Center and School of Electronics and Information Engineering, HIT. His research interests include mobile edge computing and satellite edge computing. (Email: 20B905015@stu.hit.edu.cn)

Qing GUO received the M.S. and Ph.D. degrees from Beijing University of Posts and Telecommunications and HIT in 1985 and 1998, respectively. He is currently a Professor and Dean at the School of Electronics and Information Engineering, HIT. His research interests focus on satellite communications and broadband multimedia communications. (Email: qguo@hit.edu.cn)
Received Date: 2022-09-16
Accepted Date: 2023-04-12

Available Online: 2023-07-08

Publish Date: 2023-09-05

Abstract

Abstract

Low earth orbit (LEO) satellites with wide coverage can carry mobile edge computing (MEC) servers with computing power to form the LEO satellite edge computing system, providing computing services for ground users that cannot access the core network. This paper studies the joint optimization problem of communication and computing resource in the LEO satellite edge computing system to minimize the utility function value of the system. Due to the fact that, general optimization tools cannot effectively solve this problem, this paper proposes a deep learning-based bandwidth allocation algorithm. The bandwidth allocation schemes are generated through multiple parallel deep neural networks (DNNs). The utility function values of the system are calculated according to the derived optimal CPU cycle frequency and optimal user transmission power. The bandwidth allocation scheme corresponding to the optimal system utility function value is stored in the memory to further train and improve all DNNs. The simulation results show that the proposed algorithm can achieve good convergence effect and the algorithm proposed in this paper outperforms the other four comparison algorithms with low average time cost.
- Low earth orbit satellite,
- Edge computing,
- Deep learning,
- Joint optimization

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References(25)

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