UAV Positioning for Throughput Maximization Using Deep Learning Approaches.

Yayeh Munaye Yayeh Munaye,Lin Lin,Tarekegn Tarekegn,Adege Adege

doi:10.3390/s19122775

Yayeh Munaye Yayeh Munaye, Lin Lin + Show 2 more

Open Access

https://doi.org/10.3390/s19122775

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Abstract

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.

Highlights

unmanned aerial vehicles (UAVs), which are known as drones, have received interest from different scholars.UAVs may be used in different situations, such as for security issues, disasters, and remote areas as well as on sport fields [1,2]
We propose an iterative weighted interpolation positioning (IWIP) method for measuring the distance between ground users and the UAV location scheme based on orthogonal frequency-division multiplexing (OFDM) signal transmission for the
(2) We proposed a K-means algorithm for the classification of our data collection environment into classes based on their signal strengths, and we used linear discriminant analysis (LDA) for dataset dimensionality reduction and iterative weighted interpolation positioning (IWIP) for computing each user’s distance

Summary

Introduction

UAVs, which are known as drones, have received interest from different scholars.UAVs may be used in different situations, such as for security issues, disasters, and remote areas as well as on sport fields [1,2]. To satisfy the interest of mobile users, concerned organizations have attempted to expand the coverage of long term evolution (LTE) and access points (APs) based on wireless fidelity (Wi-Fi) connections Their large consumption and the fixed location do not solve the problem well for remote areas [3,4,5]. The use of UAVs as aerial base stations (BSs) as a communication platform is important for the assistance of territorial networks This is a vital area of enhancement for contemporary technology as a key stage for fifth-generation (5G) networks and has been a main focus of researchers and service providers [6,7].

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