Joint Communication, Computing, and Caching Resource Allocation in LEO Satellite MEC Networks
Driven by the urgent requirement of ubiquitous and reliable coverage for global users, the low earth orbit (LEO) satellite network has attracted numerous attentions from the academic and industry circles. By deploying multi-access edge computing (MEC) servers in LEO satellites, computation offloadin...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10018385/ |
| _version_ | 1797945025963753472 |
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| author | Yuanyuan Hao Zhengyu Song Zhong Zheng Qian Zhang Zhongyu Miao |
| author_facet | Yuanyuan Hao Zhengyu Song Zhong Zheng Qian Zhang Zhongyu Miao |
| author_sort | Yuanyuan Hao |
| collection | DOAJ |
| description | Driven by the urgent requirement of ubiquitous and reliable coverage for global users, the low earth orbit (LEO) satellite network has attracted numerous attentions from the academic and industry circles. By deploying multi-access edge computing (MEC) servers in LEO satellites, computation offloading and content caching services can be provided for remote Internet-of-Things (IoT) devices without proximal servers. In this paper, the joint optimization of computation offloading, radio resource allocation and caching placement in LEO satellite MEC networks are investigated. The problem is formulated to minimize the total delay of all ground IoT devices while ensuring the energy, computing and caching constraints. To solve this mixed-integer and non-convex problem, a Lagrange dual decomposition (LDD)-based algorithm is proposed to obtain the closed-form optimal solution. Then, a heuristic algorithm is proposed to further reduce the computation complexity. Numerical results validate that both algorithms are effective compared to the optimal exhaustive search, the full local computing and the full MEC methods. Besides, the offloading ratio and the average delay of all IoT devices with different numbers and computing capacities of devices and satellites are also demonstrated. |
| first_indexed | 2024-04-10T20:48:51Z |
| format | Article |
| id | doaj.art-25727233fb694cd99758055eb8544819 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T20:48:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-25727233fb694cd99758055eb85448192023-01-24T00:00:43ZengIEEEIEEE Access2169-35362023-01-01116708671610.1109/ACCESS.2023.323770110018385Joint Communication, Computing, and Caching Resource Allocation in LEO Satellite MEC NetworksYuanyuan Hao0https://orcid.org/0000-0001-9841-0814Zhengyu Song1https://orcid.org/0000-0001-5959-0891Zhong Zheng2Qian Zhang3https://orcid.org/0000-0002-8331-9185Zhongyu Miao4https://orcid.org/0000-0003-1718-3600Institute of Telecommunication and Navigation Satellites, China Academy of Space Technology, Beijing, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, ChinaInstitute of Telecommunication and Navigation Satellites, China Academy of Space Technology, Beijing, ChinaInstitute of Telecommunication and Navigation Satellites, China Academy of Space Technology, Beijing, ChinaInstitute of Telecommunication and Navigation Satellites, China Academy of Space Technology, Beijing, ChinaDriven by the urgent requirement of ubiquitous and reliable coverage for global users, the low earth orbit (LEO) satellite network has attracted numerous attentions from the academic and industry circles. By deploying multi-access edge computing (MEC) servers in LEO satellites, computation offloading and content caching services can be provided for remote Internet-of-Things (IoT) devices without proximal servers. In this paper, the joint optimization of computation offloading, radio resource allocation and caching placement in LEO satellite MEC networks are investigated. The problem is formulated to minimize the total delay of all ground IoT devices while ensuring the energy, computing and caching constraints. To solve this mixed-integer and non-convex problem, a Lagrange dual decomposition (LDD)-based algorithm is proposed to obtain the closed-form optimal solution. Then, a heuristic algorithm is proposed to further reduce the computation complexity. Numerical results validate that both algorithms are effective compared to the optimal exhaustive search, the full local computing and the full MEC methods. Besides, the offloading ratio and the average delay of all IoT devices with different numbers and computing capacities of devices and satellites are also demonstrated.https://ieeexplore.ieee.org/document/10018385/LEO satellitesmulti-access edge computingcomputation offloadingresource allocationcaching |
| spellingShingle | Yuanyuan Hao Zhengyu Song Zhong Zheng Qian Zhang Zhongyu Miao Joint Communication, Computing, and Caching Resource Allocation in LEO Satellite MEC Networks IEEE Access LEO satellites multi-access edge computing computation offloading resource allocation caching |
| title | Joint Communication, Computing, and Caching Resource Allocation in LEO Satellite MEC Networks |
| title_full | Joint Communication, Computing, and Caching Resource Allocation in LEO Satellite MEC Networks |
| title_fullStr | Joint Communication, Computing, and Caching Resource Allocation in LEO Satellite MEC Networks |
| title_full_unstemmed | Joint Communication, Computing, and Caching Resource Allocation in LEO Satellite MEC Networks |
| title_short | Joint Communication, Computing, and Caching Resource Allocation in LEO Satellite MEC Networks |
| title_sort | joint communication computing and caching resource allocation in leo satellite mec networks |
| topic | LEO satellites multi-access edge computing computation offloading resource allocation caching |
| url | https://ieeexplore.ieee.org/document/10018385/ |
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