A Centralized Multi-Agent DRL-Based Trajectory Control Strategy for Unmanned Aerial Vehicle-Enabled Wireless Communications
Unmanned aerial vehicles (UAVs) are becoming increasingly popular as mobile base stations due to their flexible deployment and low-cost features, particularly for emergency communications, traffic offloading, and terrestrial communications infrastructure failures. This paper presents an autonomous t...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10654501/ |
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| author | Getaneh Berie Tarekegn Rong-Terng Juang Belayneh Abebe Tesfaw Hsin-Piao Lin Huan-Chia Hsu Robel Berie Tarekegn Li-Chia Tai |
| author_facet | Getaneh Berie Tarekegn Rong-Terng Juang Belayneh Abebe Tesfaw Hsin-Piao Lin Huan-Chia Hsu Robel Berie Tarekegn Li-Chia Tai |
| author_sort | Getaneh Berie Tarekegn |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs) are becoming increasingly popular as mobile base stations due to their flexible deployment and low-cost features, particularly for emergency communications, traffic offloading, and terrestrial communications infrastructure failures. This paper presents an autonomous trajectory control method for multiple UAVs equipped with base stations for UAV-enabled wireless communications. The objective of this work is to address the optimization challenge of maximizing both communication coverage and network throughput for ground users. The proposed multi-aerial base station trajectory control (MATC) scheme employs a two-stage learning approach. Initially, we developed a long short-term memory-based link quality estimation model to assess each user's link quality over time. The trajectory of the aerial base stations is then continuously adjusted through a centralized multi-agent deep reinforcement learning algorithm to optimize communication performance. We evaluated our proposed system using real channel measurement data, i.e., amplitude and phase signal information. Notably, the proposed approach operates solely on received signals from users, without requiring knowledge of their specific locations. The proposed MATC strategy achieves 97.41% communication coverage while maintaining satisfactory system throughput performance. Numerical results demonstrate that the proposed method significantly enhances both communication coverage and network throughput in comparison to the base line algorithms. |
| format | Article |
| id | doaj-art-f6c6f453b9a9472d948d6389ffaa7b64 |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-f6c6f453b9a9472d948d6389ffaa7b642025-01-30T00:04:42ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-0151230124110.1109/OJVT.2024.345114310654501A Centralized Multi-Agent DRL-Based Trajectory Control Strategy for Unmanned Aerial Vehicle-Enabled Wireless CommunicationsGetaneh Berie Tarekegn0https://orcid.org/0000-0002-5501-9871Rong-Terng Juang1https://orcid.org/0000-0002-9965-2396Belayneh Abebe Tesfaw2https://orcid.org/0009-0008-1123-8803Hsin-Piao Lin3https://orcid.org/0000-0003-4575-516XHuan-Chia Hsu4Robel Berie Tarekegn5https://orcid.org/0009-0001-0332-2916Li-Chia Tai6https://orcid.org/0000-0001-7042-5109Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, TaiwanInstitute of Aerospace and System Engineering, National Taipei University of Technology, Taipei, TaiwanDepartment of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei, TaiwanInstitute of Aerospace and System Engineering, National Taipei University of Technology, Taipei, TaiwanInstitute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu, TaiwanDepartment of Electrical Engineering and Computer Science, National Yang Ming Chiao Tung University, Hsinchu, TaiwanDepartment of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, TaiwanUnmanned aerial vehicles (UAVs) are becoming increasingly popular as mobile base stations due to their flexible deployment and low-cost features, particularly for emergency communications, traffic offloading, and terrestrial communications infrastructure failures. This paper presents an autonomous trajectory control method for multiple UAVs equipped with base stations for UAV-enabled wireless communications. The objective of this work is to address the optimization challenge of maximizing both communication coverage and network throughput for ground users. The proposed multi-aerial base station trajectory control (MATC) scheme employs a two-stage learning approach. Initially, we developed a long short-term memory-based link quality estimation model to assess each user's link quality over time. The trajectory of the aerial base stations is then continuously adjusted through a centralized multi-agent deep reinforcement learning algorithm to optimize communication performance. We evaluated our proposed system using real channel measurement data, i.e., amplitude and phase signal information. Notably, the proposed approach operates solely on received signals from users, without requiring knowledge of their specific locations. The proposed MATC strategy achieves 97.41% communication coverage while maintaining satisfactory system throughput performance. Numerical results demonstrate that the proposed method significantly enhances both communication coverage and network throughput in comparison to the base line algorithms.https://ieeexplore.ieee.org/document/10654501/Fair communicationslong short-term memorymulti-agent DRLtrajectory controlUAV communications |
| spellingShingle | Getaneh Berie Tarekegn Rong-Terng Juang Belayneh Abebe Tesfaw Hsin-Piao Lin Huan-Chia Hsu Robel Berie Tarekegn Li-Chia Tai A Centralized Multi-Agent DRL-Based Trajectory Control Strategy for Unmanned Aerial Vehicle-Enabled Wireless Communications IEEE Open Journal of Vehicular Technology Fair communications long short-term memory multi-agent DRL trajectory control UAV communications |
| title | A Centralized Multi-Agent DRL-Based Trajectory Control Strategy for Unmanned Aerial Vehicle-Enabled Wireless Communications |
| title_full | A Centralized Multi-Agent DRL-Based Trajectory Control Strategy for Unmanned Aerial Vehicle-Enabled Wireless Communications |
| title_fullStr | A Centralized Multi-Agent DRL-Based Trajectory Control Strategy for Unmanned Aerial Vehicle-Enabled Wireless Communications |
| title_full_unstemmed | A Centralized Multi-Agent DRL-Based Trajectory Control Strategy for Unmanned Aerial Vehicle-Enabled Wireless Communications |
| title_short | A Centralized Multi-Agent DRL-Based Trajectory Control Strategy for Unmanned Aerial Vehicle-Enabled Wireless Communications |
| title_sort | centralized multi agent drl based trajectory control strategy for unmanned aerial vehicle enabled wireless communications |
| topic | Fair communications long short-term memory multi-agent DRL trajectory control UAV communications |
| url | https://ieeexplore.ieee.org/document/10654501/ |
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