RS-DRL-based offloading policy and UAV trajectory design in F-MEC systems
For better flexibility and greater coverage areas, Unmanned Aerial Vehicles (UAVs) have been applied in Flying Mobile Edge Computing (F-MEC) systems to offer offloading services for the User Equipment (UEs). This paper considers a disaster-affected scenario where UAVs undertake the role of MEC serve...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-04-01
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| Series: | Digital Communications and Networks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864823001827 |
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| author | Yulu Yang Han Xu Zhu Jin Tiecheng Song Jing Hu Xiaoqin Song |
| author_facet | Yulu Yang Han Xu Zhu Jin Tiecheng Song Jing Hu Xiaoqin Song |
| author_sort | Yulu Yang |
| collection | DOAJ |
| description | For better flexibility and greater coverage areas, Unmanned Aerial Vehicles (UAVs) have been applied in Flying Mobile Edge Computing (F-MEC) systems to offer offloading services for the User Equipment (UEs). This paper considers a disaster-affected scenario where UAVs undertake the role of MEC servers to provide computing resources for Disaster Relief Devices (DRDs). Considering the fairness of DRDs, a max-min problem is formulated to optimize the saved time by jointly designing the trajectory of the UAVs, the offloading policy and serving time under the constraint of the UAVs' energy capacity. To solve the above non-convex problem, we first model the service process as a Markov Decision Process (MDP) with the Reward Shaping (RS) technique, and then propose a Deep Reinforcement Learning (DRL) based algorithm to find the optimal solution for the MDP. Simulations show that the proposed RS-DRL algorithm is valid and effective, and has better performance than the baseline algorithms. |
| format | Article |
| id | doaj-art-2863deff26d34c8f8c699c0a14ff8486 |
| institution | DOAJ |
| issn | 2352-8648 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj-art-2863deff26d34c8f8c699c0a14ff84862025-08-20T03:09:12ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482025-04-0111237738610.1016/j.dcan.2023.12.005RS-DRL-based offloading policy and UAV trajectory design in F-MEC systemsYulu Yang0Han Xu1Zhu Jin2Tiecheng Song3Jing Hu4Xiaoqin Song5National Mobile Communications Research Laboratory, Southeast University, Nanjing 211189, ChinaNational Mobile Communications Research Laboratory, Southeast University, Nanjing 211189, ChinaNational Mobile Communications Research Laboratory, Southeast University, Nanjing 211189, ChinaNational Mobile Communications Research Laboratory, Southeast University, Nanjing 211189, China; Corresponding author.National Mobile Communications Research Laboratory, Southeast University, Nanjing 211189, ChinaCollege of Electronic and Information Engineering/College of Integrated Circuits, Nanjing University of Aeronautics and Astronautics Nanjing 211106, ChinaFor better flexibility and greater coverage areas, Unmanned Aerial Vehicles (UAVs) have been applied in Flying Mobile Edge Computing (F-MEC) systems to offer offloading services for the User Equipment (UEs). This paper considers a disaster-affected scenario where UAVs undertake the role of MEC servers to provide computing resources for Disaster Relief Devices (DRDs). Considering the fairness of DRDs, a max-min problem is formulated to optimize the saved time by jointly designing the trajectory of the UAVs, the offloading policy and serving time under the constraint of the UAVs' energy capacity. To solve the above non-convex problem, we first model the service process as a Markov Decision Process (MDP) with the Reward Shaping (RS) technique, and then propose a Deep Reinforcement Learning (DRL) based algorithm to find the optimal solution for the MDP. Simulations show that the proposed RS-DRL algorithm is valid and effective, and has better performance than the baseline algorithms.http://www.sciencedirect.com/science/article/pii/S2352864823001827Flying mobile edge computingTask offloadingReward shapingDeep reinforcement learning |
| spellingShingle | Yulu Yang Han Xu Zhu Jin Tiecheng Song Jing Hu Xiaoqin Song RS-DRL-based offloading policy and UAV trajectory design in F-MEC systems Digital Communications and Networks Flying mobile edge computing Task offloading Reward shaping Deep reinforcement learning |
| title | RS-DRL-based offloading policy and UAV trajectory design in F-MEC systems |
| title_full | RS-DRL-based offloading policy and UAV trajectory design in F-MEC systems |
| title_fullStr | RS-DRL-based offloading policy and UAV trajectory design in F-MEC systems |
| title_full_unstemmed | RS-DRL-based offloading policy and UAV trajectory design in F-MEC systems |
| title_short | RS-DRL-based offloading policy and UAV trajectory design in F-MEC systems |
| title_sort | rs drl based offloading policy and uav trajectory design in f mec systems |
| topic | Flying mobile edge computing Task offloading Reward shaping Deep reinforcement learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352864823001827 |
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