Resource Allocation and Trajectory Optimization in OTFS-Based UAV-Assisted Mobile Edge Computing
Mobile edge computing (MEC) powered by unmanned aerial vehicles (UAVs), with the advantages of flexible deployment and wide coverage, is a promising technology to solve computationally intensive communication problems. In this paper, an orthogonal time frequency space (OTFS)-based UAV-assisted MEC s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/10/2212 |
| _version_ | 1797600355241951232 |
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| author | Wei Li Yan Guo Ning Li Hao Yuan Cuntao Liu |
| author_facet | Wei Li Yan Guo Ning Li Hao Yuan Cuntao Liu |
| author_sort | Wei Li |
| collection | DOAJ |
| description | Mobile edge computing (MEC) powered by unmanned aerial vehicles (UAVs), with the advantages of flexible deployment and wide coverage, is a promising technology to solve computationally intensive communication problems. In this paper, an orthogonal time frequency space (OTFS)-based UAV-assisted MEC system is studied, in which OTFS technology is used to mitigate the Doppler effect in UAV high-speed mobile communication. The weighted total energy consumption of the system is minimized by jointly optimizing the time division, CPU frequency allocation, transmit power allocation and flight trajectory while considering Doppler compensation. Thus, the resultant problem is a challenging nonconvex problem. We propose a joint algorithm that combines the benefits of the atomic orbital search (AOS) algorithm and convex optimization. Firstly, an improved AOS algorithm is proposed to swiftly obtain the time slot allocation and high-quality solution of the UAV optimal path. Secondly, the optimal solution for the CPU frequency and transmit power allocation is found by using Lagrangian duality and the first-order Taylor formula. Finally, the optimal solution of the original problem is iteratively obtained. The simulation results show that the weighted total energy consumption of the OTFS-based system decreases by 13.6% compared with the orthogonal frequency division multiplexing (OFDM)-based system. The weighted total energy consumption of the proposed algorithm decreases by 11.7% and 26.7% compared with convex optimization and heuristic algorithms, respectively. |
| first_indexed | 2024-03-11T03:47:58Z |
| format | Article |
| id | doaj.art-a35e5da1534b4ad5a509cfd3a62c4977 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T03:47:58Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a35e5da1534b4ad5a509cfd3a62c49772023-11-18T01:09:17ZengMDPI AGElectronics2079-92922023-05-011210221210.3390/electronics12102212Resource Allocation and Trajectory Optimization in OTFS-Based UAV-Assisted Mobile Edge ComputingWei Li0Yan Guo1Ning Li2Hao Yuan3Cuntao Liu4College of Communications Engineering, PLA Army Engineering University, Nanjing 210007, ChinaCollege of Communications Engineering, PLA Army Engineering University, Nanjing 210007, ChinaCollege of Communications Engineering, PLA Army Engineering University, Nanjing 210007, ChinaCollege of Communications Engineering, PLA Army Engineering University, Nanjing 210007, ChinaCollege of Communications Engineering, PLA Army Engineering University, Nanjing 210007, ChinaMobile edge computing (MEC) powered by unmanned aerial vehicles (UAVs), with the advantages of flexible deployment and wide coverage, is a promising technology to solve computationally intensive communication problems. In this paper, an orthogonal time frequency space (OTFS)-based UAV-assisted MEC system is studied, in which OTFS technology is used to mitigate the Doppler effect in UAV high-speed mobile communication. The weighted total energy consumption of the system is minimized by jointly optimizing the time division, CPU frequency allocation, transmit power allocation and flight trajectory while considering Doppler compensation. Thus, the resultant problem is a challenging nonconvex problem. We propose a joint algorithm that combines the benefits of the atomic orbital search (AOS) algorithm and convex optimization. Firstly, an improved AOS algorithm is proposed to swiftly obtain the time slot allocation and high-quality solution of the UAV optimal path. Secondly, the optimal solution for the CPU frequency and transmit power allocation is found by using Lagrangian duality and the first-order Taylor formula. Finally, the optimal solution of the original problem is iteratively obtained. The simulation results show that the weighted total energy consumption of the OTFS-based system decreases by 13.6% compared with the orthogonal frequency division multiplexing (OFDM)-based system. The weighted total energy consumption of the proposed algorithm decreases by 11.7% and 26.7% compared with convex optimization and heuristic algorithms, respectively.https://www.mdpi.com/2079-9292/12/10/2212orthogonal time frequency space (OTFS)6Gunmanned aerial vehicle (UAV)mobile edge computing (MEC)resource allocation |
| spellingShingle | Wei Li Yan Guo Ning Li Hao Yuan Cuntao Liu Resource Allocation and Trajectory Optimization in OTFS-Based UAV-Assisted Mobile Edge Computing Electronics orthogonal time frequency space (OTFS) 6G unmanned aerial vehicle (UAV) mobile edge computing (MEC) resource allocation |
| title | Resource Allocation and Trajectory Optimization in OTFS-Based UAV-Assisted Mobile Edge Computing |
| title_full | Resource Allocation and Trajectory Optimization in OTFS-Based UAV-Assisted Mobile Edge Computing |
| title_fullStr | Resource Allocation and Trajectory Optimization in OTFS-Based UAV-Assisted Mobile Edge Computing |
| title_full_unstemmed | Resource Allocation and Trajectory Optimization in OTFS-Based UAV-Assisted Mobile Edge Computing |
| title_short | Resource Allocation and Trajectory Optimization in OTFS-Based UAV-Assisted Mobile Edge Computing |
| title_sort | resource allocation and trajectory optimization in otfs based uav assisted mobile edge computing |
| topic | orthogonal time frequency space (OTFS) 6G unmanned aerial vehicle (UAV) mobile edge computing (MEC) resource allocation |
| url | https://www.mdpi.com/2079-9292/12/10/2212 |
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