Traffic and Obstacle-Aware UAV Positioning in Urban Environments Using Reinforcement Learning
Unmanned Aerial Vehicles (UAVs) are suited as cost-effective and adaptable platforms for carrying Wi-Fi Access Points (APs) and cellular Base Stations (BSs). Implementing aerial networks in disaster management scenarios and crowded areas can effectively enhance Quality of Service (QoS). Maintaining...
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| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10792915/ |
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| author | Kamran Shafafi Manuel Ricardo Rui Campos |
| author_facet | Kamran Shafafi Manuel Ricardo Rui Campos |
| author_sort | Kamran Shafafi |
| collection | DOAJ |
| description | Unmanned Aerial Vehicles (UAVs) are suited as cost-effective and adaptable platforms for carrying Wi-Fi Access Points (APs) and cellular Base Stations (BSs). Implementing aerial networks in disaster management scenarios and crowded areas can effectively enhance Quality of Service (QoS). Maintaining Line-of-Sight (LoS) in such environments, especially at higher frequencies, is crucial for ensuring reliable communication networks with high capacity, particularly in environments with obstacles. The main contribution of this paper is a traffic- and obstacle-aware UAV positioning algorithm named Reinforcement Learning-based Traffic and Obstacle-aware Positioning Algorithm (RLTOPA), for such environments. RLTOPA determines the optimal position of the UAV by considering the positions of ground users, the coordinates of obstacles, and the traffic demands of users. This positioning aims to maximize QoS in terms of throughput by ensuring optimal LoS between ground users and the UAV. The network performance of the proposed solution, characterized in terms of mean delay and throughput, was evaluated using the ns-3 simulator. The results show up to 95% improvement in aggregate throughput and 71% in delay without compromising fairness. |
| format | Article |
| id | doaj-art-21c7205fbcd1462abc4ce41449cbf550 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-21c7205fbcd1462abc4ce41449cbf5502025-08-20T02:49:09ZengIEEEIEEE Access2169-35362024-01-011218865218866310.1109/ACCESS.2024.351565410792915Traffic and Obstacle-Aware UAV Positioning in Urban Environments Using Reinforcement LearningKamran Shafafi0https://orcid.org/0000-0001-6211-7923Manuel Ricardo1https://orcid.org/0000-0003-1969-958XRui Campos2https://orcid.org/0000-0001-9419-6670INESC TEC, Faculdade de Engenharia, Universidade do Porto, Porto, PortugalINESC TEC, Faculdade de Engenharia, Universidade do Porto, Porto, PortugalINESC TEC, Faculdade de Engenharia, Universidade do Porto, Porto, PortugalUnmanned Aerial Vehicles (UAVs) are suited as cost-effective and adaptable platforms for carrying Wi-Fi Access Points (APs) and cellular Base Stations (BSs). Implementing aerial networks in disaster management scenarios and crowded areas can effectively enhance Quality of Service (QoS). Maintaining Line-of-Sight (LoS) in such environments, especially at higher frequencies, is crucial for ensuring reliable communication networks with high capacity, particularly in environments with obstacles. The main contribution of this paper is a traffic- and obstacle-aware UAV positioning algorithm named Reinforcement Learning-based Traffic and Obstacle-aware Positioning Algorithm (RLTOPA), for such environments. RLTOPA determines the optimal position of the UAV by considering the positions of ground users, the coordinates of obstacles, and the traffic demands of users. This positioning aims to maximize QoS in terms of throughput by ensuring optimal LoS between ground users and the UAV. The network performance of the proposed solution, characterized in terms of mean delay and throughput, was evaluated using the ns-3 simulator. The results show up to 95% improvement in aggregate throughput and 71% in delay without compromising fairness.https://ieeexplore.ieee.org/document/10792915/Unmanned aerial vehiclesUAV positioningaerial networksLoS communications technologyreinforcement learninghigh-capacity communications |
| spellingShingle | Kamran Shafafi Manuel Ricardo Rui Campos Traffic and Obstacle-Aware UAV Positioning in Urban Environments Using Reinforcement Learning IEEE Access Unmanned aerial vehicles UAV positioning aerial networks LoS communications technology reinforcement learning high-capacity communications |
| title | Traffic and Obstacle-Aware UAV Positioning in Urban Environments Using Reinforcement Learning |
| title_full | Traffic and Obstacle-Aware UAV Positioning in Urban Environments Using Reinforcement Learning |
| title_fullStr | Traffic and Obstacle-Aware UAV Positioning in Urban Environments Using Reinforcement Learning |
| title_full_unstemmed | Traffic and Obstacle-Aware UAV Positioning in Urban Environments Using Reinforcement Learning |
| title_short | Traffic and Obstacle-Aware UAV Positioning in Urban Environments Using Reinforcement Learning |
| title_sort | traffic and obstacle aware uav positioning in urban environments using reinforcement learning |
| topic | Unmanned aerial vehicles UAV positioning aerial networks LoS communications technology reinforcement learning high-capacity communications |
| url | https://ieeexplore.ieee.org/document/10792915/ |
| work_keys_str_mv | AT kamranshafafi trafficandobstacleawareuavpositioninginurbanenvironmentsusingreinforcementlearning AT manuelricardo trafficandobstacleawareuavpositioninginurbanenvironmentsusingreinforcementlearning AT ruicampos trafficandobstacleawareuavpositioninginurbanenvironmentsusingreinforcementlearning |