Optimal Positioning of Unmanned Aerial Vehicle (UAV) Base Stations Using Mixed-Integer Linear Programming
In wireless communications, traditional base stations act as the backbone for providing network connectivity to users. These base stations, however, require significant resources to construct and are therefore not suitable for remote areas and disaster scenarios. This challenge makes them unfit for...
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MDPI AG
2025-01-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/1/44 |
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| author | Gowtham Raj Veeraswamy Premkumar Bryan Van Scoy |
| author_facet | Gowtham Raj Veeraswamy Premkumar Bryan Van Scoy |
| author_sort | Gowtham Raj Veeraswamy Premkumar |
| collection | DOAJ |
| description | In wireless communications, traditional base stations act as the backbone for providing network connectivity to users. These base stations, however, require significant resources to construct and are therefore not suitable for remote areas and disaster scenarios. This challenge makes them unfit for deployment in remote areas or in disaster scenarios where fast network establishment is necessary. To address these challenges, cellular base stations installed on Unmanned Aerial Vehicles (UAVs) can be an alternative solution. UAVs provide quick deployment capability and can adapt to changing environmental situations, making them ideal for dynamic network scenarios. In this paper, we address the critical issue of UAV positioning to maximize the total user coverage, which can be formulated as a mixed-integer linear program. Given the complexity of larger-scale scenarios related to the number of users, we suggest a two-step method. First, we group users into clusters, and then we optimize the UAV positions with respect to these clusters. This approach introduces a trade-off between computational time efficiency and optimality, which can be tuned by adjusting the number of clusters. By varying the number of clusters, we balance computation time with the optimality of the UAV locations, allowing flexible deployment in diverse scenarios. |
| format | Article |
| id | doaj-art-12c85a0f1d2a4cf7adecce0da7458d31 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-12c85a0f1d2a4cf7adecce0da7458d312025-01-24T13:29:46ZengMDPI AGDrones2504-446X2025-01-01914410.3390/drones9010044Optimal Positioning of Unmanned Aerial Vehicle (UAV) Base Stations Using Mixed-Integer Linear ProgrammingGowtham Raj Veeraswamy Premkumar0Bryan Van Scoy1Department of Electrical and Computer Engineering, Miami University, Oxford, OH 45056, USADepartment of Electrical and Computer Engineering, Miami University, Oxford, OH 45056, USAIn wireless communications, traditional base stations act as the backbone for providing network connectivity to users. These base stations, however, require significant resources to construct and are therefore not suitable for remote areas and disaster scenarios. This challenge makes them unfit for deployment in remote areas or in disaster scenarios where fast network establishment is necessary. To address these challenges, cellular base stations installed on Unmanned Aerial Vehicles (UAVs) can be an alternative solution. UAVs provide quick deployment capability and can adapt to changing environmental situations, making them ideal for dynamic network scenarios. In this paper, we address the critical issue of UAV positioning to maximize the total user coverage, which can be formulated as a mixed-integer linear program. Given the complexity of larger-scale scenarios related to the number of users, we suggest a two-step method. First, we group users into clusters, and then we optimize the UAV positions with respect to these clusters. This approach introduces a trade-off between computational time efficiency and optimality, which can be tuned by adjusting the number of clusters. By varying the number of clusters, we balance computation time with the optimality of the UAV locations, allowing flexible deployment in diverse scenarios.https://www.mdpi.com/2504-446X/9/1/44UAVMILPcommunicationoptimizationclustering |
| spellingShingle | Gowtham Raj Veeraswamy Premkumar Bryan Van Scoy Optimal Positioning of Unmanned Aerial Vehicle (UAV) Base Stations Using Mixed-Integer Linear Programming Drones UAV MILP communication optimization clustering |
| title | Optimal Positioning of Unmanned Aerial Vehicle (UAV) Base Stations Using Mixed-Integer Linear Programming |
| title_full | Optimal Positioning of Unmanned Aerial Vehicle (UAV) Base Stations Using Mixed-Integer Linear Programming |
| title_fullStr | Optimal Positioning of Unmanned Aerial Vehicle (UAV) Base Stations Using Mixed-Integer Linear Programming |
| title_full_unstemmed | Optimal Positioning of Unmanned Aerial Vehicle (UAV) Base Stations Using Mixed-Integer Linear Programming |
| title_short | Optimal Positioning of Unmanned Aerial Vehicle (UAV) Base Stations Using Mixed-Integer Linear Programming |
| title_sort | optimal positioning of unmanned aerial vehicle uav base stations using mixed integer linear programming |
| topic | UAV MILP communication optimization clustering |
| url | https://www.mdpi.com/2504-446X/9/1/44 |
| work_keys_str_mv | AT gowthamrajveeraswamypremkumar optimalpositioningofunmannedaerialvehicleuavbasestationsusingmixedintegerlinearprogramming AT bryanvanscoy optimalpositioningofunmannedaerialvehicleuavbasestationsusingmixedintegerlinearprogramming |