Energy Efficiency Maximization in RISs-Assisted UAVs-Based Edge Computing Network Using Deep Reinforcement Learning
Edge Computing (EC) pushes computational capability to the Terrestrial Devices (TDs), providing more efficient and faster computing solutions. Unmanned Aerial Vehicles (UAVs) equipped with EC servers can be flexibly deployed, even in complex terrains, to provide mobile computing services at all time...
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Tsinghua University Press
2024-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020022 |
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| author | Chuanwen Luo Jian Zhang Jianxiong Guo Yi Hong Zhibo Chen Shuyang Gu |
| author_facet | Chuanwen Luo Jian Zhang Jianxiong Guo Yi Hong Zhibo Chen Shuyang Gu |
| author_sort | Chuanwen Luo |
| collection | DOAJ |
| description | Edge Computing (EC) pushes computational capability to the Terrestrial Devices (TDs), providing more efficient and faster computing solutions. Unmanned Aerial Vehicles (UAVs) equipped with EC servers can be flexibly deployed, even in complex terrains, to provide mobile computing services at all times. Meanwhile, UAVs can establish an air-to-ground line-of-sight link with TDs to improve the quality of communication link. However, the UAV-to-TD link may be obstructed by ground obstacles such as buildings or trees, leading to sub-optimal data transmission rates. To surmount this issue, Reconfigurable Intelligent Surfaces (RISs) emerge as a promising technology capable of intelligently reflecting signals to enhance communication quality between UAVs and TDs. In this paper, we consider the RISs-assisted multi-UAVs collaborative edge Computing Network (RUCN) in urban environment, in which we study the computational offloading problem. Our goal is to maximize the overall energy efficiency of UAVs by jointly optimizing the flight duration and trajectories of UAVs, and the phase shifts of RISs. It is worth noting that this problem has been formally established as NP-hard. Therefore, we propose the Deep Deterministic Policy Gradients based UAV Trajectory and RIS Phase shift optimization algorithm (UTRP-DDPG) to solve this complex optimization challenge. The results of extensive numerical experiments show that the proposed algorithm outperforms the other benchmark algorithms under various parameter settings. Specially, the UTRP-DDPG algorithm improves the UAV energy efficiency by at least 2% compared to DQN algorithm. |
| format | Article |
| id | doaj-art-4eb8d63a5e4746e29c6eaac7fe47294e |
| institution | OA Journals |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-4eb8d63a5e4746e29c6eaac7fe47294e2025-08-20T02:05:35ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-12-01741065108310.26599/BDMA.2024.9020022Energy Efficiency Maximization in RISs-Assisted UAVs-Based Edge Computing Network Using Deep Reinforcement LearningChuanwen Luo0Jian Zhang1Jianxiong Guo2Yi Hong3Zhibo Chen4Shuyang Gu5School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China, and also with the Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, China, and also with the Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, ChinaAdvanced Institute of Natural Sciences, Beijing Normal University, Zhuhai 519087, China, and also with the Guangdong Key Lab of AI and Multi-Modal Data Processing, BNU-HKBU United International College, Zhuhai 519087, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, China, and also with the Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, China, and also with the Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, ChinaDepartment of Computer Information Systems, Texas A&M University-Central Texas, Killeen, TX 76549, USAEdge Computing (EC) pushes computational capability to the Terrestrial Devices (TDs), providing more efficient and faster computing solutions. Unmanned Aerial Vehicles (UAVs) equipped with EC servers can be flexibly deployed, even in complex terrains, to provide mobile computing services at all times. Meanwhile, UAVs can establish an air-to-ground line-of-sight link with TDs to improve the quality of communication link. However, the UAV-to-TD link may be obstructed by ground obstacles such as buildings or trees, leading to sub-optimal data transmission rates. To surmount this issue, Reconfigurable Intelligent Surfaces (RISs) emerge as a promising technology capable of intelligently reflecting signals to enhance communication quality between UAVs and TDs. In this paper, we consider the RISs-assisted multi-UAVs collaborative edge Computing Network (RUCN) in urban environment, in which we study the computational offloading problem. Our goal is to maximize the overall energy efficiency of UAVs by jointly optimizing the flight duration and trajectories of UAVs, and the phase shifts of RISs. It is worth noting that this problem has been formally established as NP-hard. Therefore, we propose the Deep Deterministic Policy Gradients based UAV Trajectory and RIS Phase shift optimization algorithm (UTRP-DDPG) to solve this complex optimization challenge. The results of extensive numerical experiments show that the proposed algorithm outperforms the other benchmark algorithms under various parameter settings. Specially, the UTRP-DDPG algorithm improves the UAV energy efficiency by at least 2% compared to DQN algorithm.https://www.sciopen.com/article/10.26599/BDMA.2024.9020022edge computingcomputing offloadingunmanned aerial vehicle (uav)reconfigurable intelligent surface (ris)deep reinforcement learning |
| spellingShingle | Chuanwen Luo Jian Zhang Jianxiong Guo Yi Hong Zhibo Chen Shuyang Gu Energy Efficiency Maximization in RISs-Assisted UAVs-Based Edge Computing Network Using Deep Reinforcement Learning Big Data Mining and Analytics edge computing computing offloading unmanned aerial vehicle (uav) reconfigurable intelligent surface (ris) deep reinforcement learning |
| title | Energy Efficiency Maximization in RISs-Assisted UAVs-Based Edge Computing Network Using Deep Reinforcement Learning |
| title_full | Energy Efficiency Maximization in RISs-Assisted UAVs-Based Edge Computing Network Using Deep Reinforcement Learning |
| title_fullStr | Energy Efficiency Maximization in RISs-Assisted UAVs-Based Edge Computing Network Using Deep Reinforcement Learning |
| title_full_unstemmed | Energy Efficiency Maximization in RISs-Assisted UAVs-Based Edge Computing Network Using Deep Reinforcement Learning |
| title_short | Energy Efficiency Maximization in RISs-Assisted UAVs-Based Edge Computing Network Using Deep Reinforcement Learning |
| title_sort | energy efficiency maximization in riss assisted uavs based edge computing network using deep reinforcement learning |
| topic | edge computing computing offloading unmanned aerial vehicle (uav) reconfigurable intelligent surface (ris) deep reinforcement learning |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020022 |
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