Next Article in Journal
Computational Efficiency-Based Adaptive Tracking Control for Robotic Manipulators with Unknown Input Bouc–Wen Hysteresis
Next Article in Special Issue
Fault Detection in Power Equipment via an Unmanned Aerial System Using Multi Modal Data
Previous Article in Journal
Partition-Based Joint Placement of Gateway and Controller in SDN-Enabled Integrated Satellite-Terrestrial Networks
Previous Article in Special Issue
Comparison of Leaf Area Index, Surface Temperature, and Actual Evapotranspiration Estimated Using the METRIC Model and In Situ Measurements
Open AccessArticle

UAV Positioning for Throughput Maximization Using Deep Learning Approaches

Department of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, Taiwan
Department of Electronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Author to whom correspondence should be addressed.
Sensors 2019, 19(12), 2775;
Received: 29 April 2019 / Revised: 13 June 2019 / Accepted: 17 June 2019 / Published: 20 June 2019
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. View Full-Text
Keywords: user throughput; maximization; UAV; positioning; deep learning (DL) user throughput; maximization; UAV; positioning; deep learning (DL)
Show Figures

Figure 1

MDPI and ACS Style

Munaye, Y.Y.; Lin, H.-P.; Adege, A.B.; Tarekegn, G.B. UAV Positioning for Throughput Maximization Using Deep Learning Approaches. Sensors 2019, 19, 2775.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop