Joint Trajectory Design, Task Data, and Computing Resource Allocations for NOMA-Based and UAV-Assisted Mobile Edge Computing
Mobile edge computing (MEC) has been considered as a promising technique to address the explosively growing computation-intensive applications. Thanks to the flexibility of the unmanned aerial vehicles (UAVs), the UAV-assisted MEC can serve mobile terminals (MTs) effectively, i.e., the computing ser...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8809879/ |
| _version_ | 1818412553198370816 |
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| author | Xianbang Diao Jianchao Zheng Yuan Wu Yueming Cai Alagan Anpalagan |
| author_facet | Xianbang Diao Jianchao Zheng Yuan Wu Yueming Cai Alagan Anpalagan |
| author_sort | Xianbang Diao |
| collection | DOAJ |
| description | Mobile edge computing (MEC) has been considered as a promising technique to address the explosively growing computation-intensive applications. Thanks to the flexibility of the unmanned aerial vehicles (UAVs), the UAV-assisted MEC can serve mobile terminals (MTs) effectively, i.e., the computing server installed on the UAV can flexibly change its location to serve MTs. Moreover, since non-orthogonal multiple access (NOMA) is able to accommodate massive connectivity, the NOMA-based and UAV-assisted MEC can provide flexible computing services for MTs in large-scale access networks (e.g., sensor networks and Internet of Things). However, due to the diversity of the UAV's trajectory and the interference among MTs introduced by NOMA, the performance (e.g., energy consumption and delay) of the NOMA-based and UAV-assisted MEC system is adversely affected. Therefore, in this paper, we formulate an optimization problem to minimize the largest energy consumption among MTs by jointly optimizing the trajectory, task data and computing resource allocations, and then propose an iterative algorithm to solve the optimization problem. Furthermore, to minimize the largest energy consumption among MTs with lower complexity, we propose a fixed point service scheme and optimize the location of the fixed point. The simulation results show that the proposed optimization algorithms can effectively reduce the largest energy consumption among MTs and ensure the fairness among MTs. |
| first_indexed | 2024-12-14T10:49:08Z |
| format | Article |
| id | doaj.art-cda1244196ea40c0be5d4a494bcc1958 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T10:49:08Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cda1244196ea40c0be5d4a494bcc19582022-12-21T23:05:20ZengIEEEIEEE Access2169-35362019-01-01711744811745910.1109/ACCESS.2019.29364378809879Joint Trajectory Design, Task Data, and Computing Resource Allocations for NOMA-Based and UAV-Assisted Mobile Edge ComputingXianbang Diao0Jianchao Zheng1https://orcid.org/0000-0001-6959-0406Yuan Wu2Yueming Cai3Alagan Anpalagan4https://orcid.org/0000-0002-6646-6052College of Communications Engineering, Army Engineering University of PLA, Nanjing, ChinaCollege of Communications Engineering, Army Engineering University of PLA, Nanjing, ChinaState Key Laboratory of Internet of Things for Smart City, University of Macau, Taipa, ChinaCollege of Communications Engineering, Army Engineering University of PLA, Nanjing, ChinaDepartment of Electrical and Computer Engineering, Ryerson University, Toronto, ON, CanadaMobile edge computing (MEC) has been considered as a promising technique to address the explosively growing computation-intensive applications. Thanks to the flexibility of the unmanned aerial vehicles (UAVs), the UAV-assisted MEC can serve mobile terminals (MTs) effectively, i.e., the computing server installed on the UAV can flexibly change its location to serve MTs. Moreover, since non-orthogonal multiple access (NOMA) is able to accommodate massive connectivity, the NOMA-based and UAV-assisted MEC can provide flexible computing services for MTs in large-scale access networks (e.g., sensor networks and Internet of Things). However, due to the diversity of the UAV's trajectory and the interference among MTs introduced by NOMA, the performance (e.g., energy consumption and delay) of the NOMA-based and UAV-assisted MEC system is adversely affected. Therefore, in this paper, we formulate an optimization problem to minimize the largest energy consumption among MTs by jointly optimizing the trajectory, task data and computing resource allocations, and then propose an iterative algorithm to solve the optimization problem. Furthermore, to minimize the largest energy consumption among MTs with lower complexity, we propose a fixed point service scheme and optimize the location of the fixed point. The simulation results show that the proposed optimization algorithms can effectively reduce the largest energy consumption among MTs and ensure the fairness among MTs.https://ieeexplore.ieee.org/document/8809879/Mobile edge computingnon-orthogonal multiple accessunmanned aerial vehiclestrajectory design |
| spellingShingle | Xianbang Diao Jianchao Zheng Yuan Wu Yueming Cai Alagan Anpalagan Joint Trajectory Design, Task Data, and Computing Resource Allocations for NOMA-Based and UAV-Assisted Mobile Edge Computing IEEE Access Mobile edge computing non-orthogonal multiple access unmanned aerial vehicles trajectory design |
| title | Joint Trajectory Design, Task Data, and Computing Resource Allocations for NOMA-Based and UAV-Assisted Mobile Edge Computing |
| title_full | Joint Trajectory Design, Task Data, and Computing Resource Allocations for NOMA-Based and UAV-Assisted Mobile Edge Computing |
| title_fullStr | Joint Trajectory Design, Task Data, and Computing Resource Allocations for NOMA-Based and UAV-Assisted Mobile Edge Computing |
| title_full_unstemmed | Joint Trajectory Design, Task Data, and Computing Resource Allocations for NOMA-Based and UAV-Assisted Mobile Edge Computing |
| title_short | Joint Trajectory Design, Task Data, and Computing Resource Allocations for NOMA-Based and UAV-Assisted Mobile Edge Computing |
| title_sort | joint trajectory design task data and computing resource allocations for noma based and uav assisted mobile edge computing |
| topic | Mobile edge computing non-orthogonal multiple access unmanned aerial vehicles trajectory design |
| url | https://ieeexplore.ieee.org/document/8809879/ |
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