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Unmanned Aerial Vehicle (UAV)-Based Solutions for 5G and Beyond

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 7830

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Special Issue Editor

Dr. Jeongmin Kang

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Guest Editor

Department of Electrical Engineering, Linköping University, Linköping, Sweden
Interests: aerial navigation; data driven; UAV photogrammetry; image processing; deep neural network

Special Issue Information

Dear Colleagues,

The importance of unmanned aerial vehicles (UAVs) for 5G and beyond continues to increase, thanks to their position as a key technology for real-world applications with stable and high-performance navigation. Despite numerous demands, wireless network-enabled UAV navigation solutions face great challenges, such as three-dimensional high dynamicity, hardware resources, external interference and disturbances. Recently, research on UAV navigation and wireless networks has been made significant strides toward useful advances. This Special Issue aims to share state-of-the-art works on base solution and applications of UAV navigation for 5G and beyond, with particular focus on the following topics (but is not limited to them):

Wireless network design;
Signal processing;
UAV navigation system;
Perception, estimation, and tracking for UAV;
Multi-UAV control system;
Machine learning based application.

Dr. Jeongmin Kang
Guest Editor

Manuscript Submission Information

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Keywords

unmanned aerial vehicles
5G network
navigation of UAV
control of UAV
machine learning

Published Papers (4 papers)

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Research

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29 pages, 33757 KiB

Open AccessArticle

Blasthole Location Detection Using Support Vector Machine and Convolutional Neural Networks on UAV Images and Photogrammetry Models

by Jorge Valencia, Ebrahim Emami, Rushikesh Battulwar, Ankit Jha, Jose A. Gomez, Amin Moniri-Morad and Javad Sattarvand

Electronics 2024, 13(7), 1291; https://doi.org/10.3390/electronics13071291 - 30 Mar 2024

Viewed by 489

Abstract

Identifying the as-drilled location of blastholes is crucial for achieving optimal blasting results. This research proposes a novel integrated methodology to control drilling accuracy in open-pit mines. This approach is developed by combining aerial drone images with machine learning techniques. The study investigates the viability of photogrammetry combined with machine learning techniques, particularly Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), for automatically detecting blastholes in photogrammetry representations of blast patterns. To verify the hypothesis that machine learning can detect blastholes in images as effectively as humans, various datasets (drone images) were obtained from different mine sites in Nevada, USA. The images were processed to create photogrammetry mapping of the drill patterns. In this process, thousands of patches were extracted and augmented from the photogrammetry representations. Those patches were then used to train and test different CNN architectures optimized to locate blastholes. After reaching an acceptable level of accuracy during the training process, the model was tested using a piece of completely unknown data (testing dataset). The high recall, precision, and percentage of detected blastholes prove that the combination of SVM, CNN, and photogrammetry (PHG) is an effective methodology for detecting blastholes on photogrammetry maps. Full article

(This article belongs to the Special Issue Unmanned Aerial Vehicle (UAV)-Based Solutions for 5G and Beyond)

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22 pages, 4288 KiB

Open AccessArticle

Dynamic Models with Sigmoid Corrections to Generation of an Achievable 4D-Trajectory for a UAV and Estimating Wind Disturbances

by Svetlana A. Krasnova, Julia G. Kokunko and Victor A. Utkin

Electronics 2023, 12(10), 2280; https://doi.org/10.3390/electronics12102280 - 18 May 2023

Cited by 2 | Viewed by 866

Abstract

For an unmanned aerial vehicle (UAV) of an aircraft type, the problems of planning achievable trajectories as well as robust control under wind disturbances are considered. A computationally simple method for compiling a primary non-smooth 4D trajectory is proposed. Its segments connect the given waypoints and determine the desired average velocity in various sections. Instead of time-consuming methods of analytical smoothing of broken path joints using polynomials, a tracking differentiator with S-shaped smooth and limited sigmoid corrective actions is developed. This virtual dynamic model provides natural smoothing of the primary trajectory considering the design constraints on the velocity, acceleration, and thrust of the UAV. The tracking differentiator variables create an achievable trajectory and are used to synthesize the UAV tracking system. To compensate for the action of wind disturbances on the UAV, a disturbance observer is developed. It is a replica of the equations of the control plant model, which are directly affected by external uncontrolled disturbances. These algorithms also use sigmoid corrections. Unlike standard disturbances observers, this approach does not require the development of a dynamic model of external disturbances and does not assume their smoothness. The effectiveness of the developed algorithms was confirmed by numerical simulation. Full article

(This article belongs to the Special Issue Unmanned Aerial Vehicle (UAV)-Based Solutions for 5G and Beyond)

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20 pages, 9308 KiB

Open AccessArticle

HD/FD Cooperative NOMA under UAV Deployment for a Novel Disaster-Management Model

by Rampravesh Kumar and Sanjay Kumar

Electronics 2023, 12(3), 513; https://doi.org/10.3390/electronics12030513 - 19 Jan 2023

Cited by 2 | Viewed by 1464

Abstract

This work presents a novel system model consisting of an unmanned aerial vehicle (UAV) equipped with a half/full-duplex relay (HDR/FDR) operating as a near-user in the downlink non-orthogonal multiple access (NOMA) systems. In a disaster situation, there is no direct connectivity of the active base station (BS) to the far user due to the out-of-coverage range. Therefore, UAV communication is established to aid the transmission from the same BS to the far user via the UAV. To quantify the effect, outage probability and throughput expressions in the exact and asymptotic forms were developed over the Weibull distribution (WD) fading channel. Additionally, the separation distance of the UAV from the base station is considered to quantify the effect. In particular, this paper helps to determine the optimal location of the UAV deployment from the BS at a fixed height from the ground to either maximize the far-user throughput or attain far-user throughput over different conditions of the WD fading channel. In addition, the performance results of the UAV-HDR/FDR-NOMA system are compared with those of a conventional downlink orthogonal multiple access (OMA) system. The comparison reveals that the UAV-HDR/FDR-NOMA systems outperform corresponding OMA systems in terms of outage probability and throughput over different values of the Weibull shaping index. The analytical results are then validated through numerical simulations on MATLAB. Full article

(This article belongs to the Special Issue Unmanned Aerial Vehicle (UAV)-Based Solutions for 5G and Beyond)

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Review

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19 pages, 1675 KiB

Open AccessReview

A Review of Indoor Positioning Systems for UAV Localization with Machine Learning Algorithms

by Chamali Sandamini, Madduma Wellalage Pasan Maduranga, Valmik Tilwari, Jamaiah Yahaya, Faizan Qamar, Quang Ngoc Nguyen and Siti Rohana Ahmad Ibrahim

Electronics 2023, 12(7), 1533; https://doi.org/10.3390/electronics12071533 - 24 Mar 2023

Cited by 10 | Viewed by 4153

Abstract

The potential of indoor unmanned aerial vehicle (UAV) localization is paramount for diversified applications within large industrial sites, such as hangars, malls, warehouses, production lines, etc. In such real-time applications, autonomous UAV location is required constantly. This paper comprehensively reviews radio signal-based wireless technologies, machine learning (ML) algorithms and ranging techniques that are used for UAV indoor positioning systems. UAV indoor localization typically relies on vision-based techniques coupled with inertial sensing in indoor Global Positioning System (GPS)-denied situations, such as visual odometry or simultaneous localization and mapping employing 2D/3D cameras or laser rangefinders. This work critically reviews the research and systems related to mini-UAV localization in indoor environments. It also provides a guide and technical comparison perspective of different technologies, presenting their main advantages and disadvantages. Finally, it discusses various open issues and highlights future directions for UAV indoor localization. Full article

(This article belongs to the Special Issue Unmanned Aerial Vehicle (UAV)-Based Solutions for 5G and Beyond)

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