UAV Positioning for Throughput Maximization Using Deep Learning Approaches
The use of unmanned aerial vehicles (UAVs) as a communication platform has great practical importance for future wireless networks, especially for on-demand deployment for temporary and emergency conditions. The user throughput estimation in a wireless system depends on the data traffic load and the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/12/2775 |
| _version_ | 1811304996093820928 |
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| author | Yirga Yayeh Munaye Hsin-Piao Lin Abebe Belay Adege Getaneh Berie Tarekegn |
| author_facet | Yirga Yayeh Munaye Hsin-Piao Lin Abebe Belay Adege Getaneh Berie Tarekegn |
| author_sort | Yirga Yayeh Munaye |
| collection | DOAJ |
| description | The use of unmanned aerial vehicles (UAVs) as a communication platform has great practical importance for future wireless networks, especially for on-demand deployment for temporary and emergency conditions. The user throughput estimation in a wireless system depends on the data traffic load and the available capacity to support that load. In UAV-assisted communication, the position of the UAV is one major factor that affects the capacity available to the data flows being served. This study applies multi-layer perceptron (MLP) and long short term memory (LSTM) approaches to determine the position of a UAV that maximizes the overall system performance and user throughput. To analyze and evaluate the system performance, we apply the hybrid of MLP-LSTM for classification regression tasks and K-means algorithms for automatic clustering of classes. The implementation of our work is done through TensorFlow packages. The performance of our proposed system is compared with other approaches to give accurate and novel results for both classification and regression tasks of the user throughput maximization and UAV positioning. According to the results, 98% of the user throughput maximization accuracy is correctly classified. Moreover, the UAV positioning provides accuracy levels of 94.73%, 98.33%, and 99.53% for original datasets (scenario 1), reduced features on the estimated values of user throughput at each grid point (scenario 2), and reduced feature datasets collected on different days and grid points achieved maximum throughput (scenario 3), respectively. |
| first_indexed | 2024-04-13T08:17:25Z |
| format | Article |
| id | doaj.art-e615e08fd5c74645881428a13a0d41ae |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:17:25Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e615e08fd5c74645881428a13a0d41ae2022-12-22T02:54:45ZengMDPI AGSensors1424-82202019-06-011912277510.3390/s19122775s19122775UAV Positioning for Throughput Maximization Using Deep Learning ApproachesYirga Yayeh Munaye0Hsin-Piao Lin1Abebe Belay Adege2Getaneh Berie Tarekegn3Department of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Electronic Engineering, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, TaiwanThe use of unmanned aerial vehicles (UAVs) as a communication platform has great practical importance for future wireless networks, especially for on-demand deployment for temporary and emergency conditions. The user throughput estimation in a wireless system depends on the data traffic load and the available capacity to support that load. In UAV-assisted communication, the position of the UAV is one major factor that affects the capacity available to the data flows being served. This study applies multi-layer perceptron (MLP) and long short term memory (LSTM) approaches to determine the position of a UAV that maximizes the overall system performance and user throughput. To analyze and evaluate the system performance, we apply the hybrid of MLP-LSTM for classification regression tasks and K-means algorithms for automatic clustering of classes. The implementation of our work is done through TensorFlow packages. The performance of our proposed system is compared with other approaches to give accurate and novel results for both classification and regression tasks of the user throughput maximization and UAV positioning. According to the results, 98% of the user throughput maximization accuracy is correctly classified. Moreover, the UAV positioning provides accuracy levels of 94.73%, 98.33%, and 99.53% for original datasets (scenario 1), reduced features on the estimated values of user throughput at each grid point (scenario 2), and reduced feature datasets collected on different days and grid points achieved maximum throughput (scenario 3), respectively.https://www.mdpi.com/1424-8220/19/12/2775user throughputmaximizationUAVpositioningdeep learning (DL) |
| spellingShingle | Yirga Yayeh Munaye Hsin-Piao Lin Abebe Belay Adege Getaneh Berie Tarekegn UAV Positioning for Throughput Maximization Using Deep Learning Approaches Sensors user throughput maximization UAV positioning deep learning (DL) |
| title | UAV Positioning for Throughput Maximization Using Deep Learning Approaches |
| title_full | UAV Positioning for Throughput Maximization Using Deep Learning Approaches |
| title_fullStr | UAV Positioning for Throughput Maximization Using Deep Learning Approaches |
| title_full_unstemmed | UAV Positioning for Throughput Maximization Using Deep Learning Approaches |
| title_short | UAV Positioning for Throughput Maximization Using Deep Learning Approaches |
| title_sort | uav positioning for throughput maximization using deep learning approaches |
| topic | user throughput maximization UAV positioning deep learning (DL) |
| url | https://www.mdpi.com/1424-8220/19/12/2775 |
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