Trajectory optimization for UAV-assisted relay over 5G networks based on reinforcement learning framework
Abstract With the integration of unmanned aerial vehicles (UAVs) into fifth generation (5G) networks, UAVs are used in many applications since they enhance coverage and capacity. To increase wireless communication resources, it is crucial to study the trajectory of UAV-assisted relay. In this paper,...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-07-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13638-023-02268-x |
| _version_ | 1797789969268342784 |
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| author | Sara M. M. Abohashish Rawya Y. Rizk E. I. Elsedimy |
| author_facet | Sara M. M. Abohashish Rawya Y. Rizk E. I. Elsedimy |
| author_sort | Sara M. M. Abohashish |
| collection | DOAJ |
| description | Abstract With the integration of unmanned aerial vehicles (UAVs) into fifth generation (5G) networks, UAVs are used in many applications since they enhance coverage and capacity. To increase wireless communication resources, it is crucial to study the trajectory of UAV-assisted relay. In this paper, an energy-efficient UAV trajectory for uplink communication is studied, where a UAV serves as a mobile relay to maintain the communication between ground user equipment (UE) and a macro base station. This paper proposes a UAV Trajectory Optimization (UAV-TO) scheme for load balancing based on Reinforcement Learning (RL). The proposed scheme utilizes load balancing to maximize energy efficiency for multiple UEs in order to increase network resource utilization. To deal with nonconvex optimization, the RL framework is used to optimize the trajectory UAV. Both model-based and model-free approaches of RL are utilized to solve the optimization problem, considering line of sight and non-line of sight channel models. In addition, the network load distribution is calculated. The simulation results demonstrate the effectiveness of the proposed scheme under different path losses and different flight durations. The results show a significant improvement in performance compared to the existing methods. |
| first_indexed | 2024-03-13T01:58:06Z |
| format | Article |
| id | doaj.art-27f660174f6c4cd09d28508aefa375ed |
| institution | Directory Open Access Journal |
| issn | 1687-1499 |
| language | English |
| last_indexed | 2024-03-13T01:58:06Z |
| publishDate | 2023-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj.art-27f660174f6c4cd09d28508aefa375ed2023-07-02T11:03:55ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992023-07-012023112810.1186/s13638-023-02268-xTrajectory optimization for UAV-assisted relay over 5G networks based on reinforcement learning frameworkSara M. M. Abohashish0Rawya Y. Rizk1E. I. Elsedimy2Department of System and Information Technology, Faculty of Management Technology and Information Systems, Port Said UniversityElectrical Engineering Department, Port Said UniversityDepartment of System and Information Technology, Faculty of Management Technology and Information Systems, Port Said UniversityAbstract With the integration of unmanned aerial vehicles (UAVs) into fifth generation (5G) networks, UAVs are used in many applications since they enhance coverage and capacity. To increase wireless communication resources, it is crucial to study the trajectory of UAV-assisted relay. In this paper, an energy-efficient UAV trajectory for uplink communication is studied, where a UAV serves as a mobile relay to maintain the communication between ground user equipment (UE) and a macro base station. This paper proposes a UAV Trajectory Optimization (UAV-TO) scheme for load balancing based on Reinforcement Learning (RL). The proposed scheme utilizes load balancing to maximize energy efficiency for multiple UEs in order to increase network resource utilization. To deal with nonconvex optimization, the RL framework is used to optimize the trajectory UAV. Both model-based and model-free approaches of RL are utilized to solve the optimization problem, considering line of sight and non-line of sight channel models. In addition, the network load distribution is calculated. The simulation results demonstrate the effectiveness of the proposed scheme under different path losses and different flight durations. The results show a significant improvement in performance compared to the existing methods.https://doi.org/10.1186/s13638-023-02268-xReinforcement learningSustainable development goalsTrajectory optimization UAVs |
| spellingShingle | Sara M. M. Abohashish Rawya Y. Rizk E. I. Elsedimy Trajectory optimization for UAV-assisted relay over 5G networks based on reinforcement learning framework EURASIP Journal on Wireless Communications and Networking Reinforcement learning Sustainable development goals Trajectory optimization UAVs |
| title | Trajectory optimization for UAV-assisted relay over 5G networks based on reinforcement learning framework |
| title_full | Trajectory optimization for UAV-assisted relay over 5G networks based on reinforcement learning framework |
| title_fullStr | Trajectory optimization for UAV-assisted relay over 5G networks based on reinforcement learning framework |
| title_full_unstemmed | Trajectory optimization for UAV-assisted relay over 5G networks based on reinforcement learning framework |
| title_short | Trajectory optimization for UAV-assisted relay over 5G networks based on reinforcement learning framework |
| title_sort | trajectory optimization for uav assisted relay over 5g networks based on reinforcement learning framework |
| topic | Reinforcement learning Sustainable development goals Trajectory optimization UAVs |
| url | https://doi.org/10.1186/s13638-023-02268-x |
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