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Drones
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Recent Advances in Unmanned Aerial Vehicles for Next Generation Wireless...
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Recent Advances in Unmanned Aerial Vehicles for Next Generation Wireless Communications Networks

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Communications".

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 16692

Special Issue Editors

Dr. Wali Ullah Khan

E-Mail Website1 Website2
Guest Editor
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg
Interests: convex/nonconvex optimizations; wireless communication; 5G/6G; ambient backscatter communications; intelligent reconfigurable surfaces; artificial intelligence/machine learning; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Khaled Rabie

E-Mail Website
Guest Editor
Department of Engineering, Manchester Metropolitan University, Manchester M15GD, UK
Interests: wireless communications; IoT; 6G; smart manufacturing
Special Issues, Collections and Topics in MDPI journals
Dr. Xingwang Li

E-Mail Website
Guest Editor
School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Interests: non-orthogonal multiple access (NOMA) schemes; physical layer security; backscatter communication for internet of things; reconfigurable intelligent metasurfaces for 6G communications; ultra-reliable and low-latency communication (URLLC); unmanned aerial vehicle (UAV) communication; hardware-constrained communication systems; unmanned aerial vehicle communication; multiple-input multiple-output (MIMO); energy harvesting
Special Issues, Collections and Topics in MDPI journals
Dr. Dinh-Thuan Do

E-Mail Website
Guest Editor
School of Engineering, University of Mount Union, Alliance, OH 44601-3993, USA
Interests: ML/federated learning in wireless systems; heterogeneous networks; massive MIMO; reconfigurable intelligent surface-assisted networks; mmWave communication networks; energy harvesting; full-duplex communications; cognitive radio; small cell; non-orthogonal multiple access (NOMA); physical layer security; UAV networks; visible light communication; IoT system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Due to the high mobility and flexible deployment of Unmanned Aerial Vehicles (UAVs) in an open environment, they have generated enormous interest in civil and military applications aiming to foster agile communications and enable ubiquitous connectivity. Dominant line-of-sight communication links are a huge benefit of UAV communications; however, UAVs can be more vulnerable to adversarial eavesdropping attacks.

The internet of things (IoT) comprises wearables, smartphones, simple sensor networks, UAVs, and autonomous vehicles. According to the forecast of IoT Analytics, there will be 30 billion IoT devices by 2025. Among them, an UAV—often referred to as a drone—can perform autonomous flight missions or be operated by a pilot from a ground station. UAVs can be used for various applications due to their adaptability, low acquisition and maintenance costs, high mobility, and hovering capability. Since their invention, UAVs have been considered for military purposes, including simple but dangerous tasks such as monitoring and attacking hostile targets. They are also used to grow civilian operations such as rescue efforts, cargo transport, resource exploitation, aerial photography, agriculture, traffic management, and communications. Therefore, it is important to study the latest advancements in UAV communications, such as integration of next-generation technologies into UAV communications. These technologies are intelligent reflecting surfaces, backscatter communications, artificial intelligence/machine learning, integrated sensing and communications, semantic communications, mega LEO constellations, optimal resource allocation, and underwater communications. This Special Issue aims to provide selected contributions on advances in UAV communications.

Dr. Wali Ullah Khan
Dr. Khaled Rabie
Dr. Xingwang Li
Dr. Dinh-Thuan Do
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • advanced signal processing for UAV communications
  • intelligent reflecting surfaces for UAV communications
  • backscatter communications in UAV communications
  • artificial intelligence/ machine learning for UAV communications
  • integrated sensing for UAV communications
  • semantic communications in UAV communications
  • integration of UAV communications with space and terrestrial networks
  • physical layer security in UAV communications
  • non-orthogonal/ rate splitting multiple access for UAV communications
  • resource optimization for UAV communications

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Published Papers (4 papers)

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Research

19 pages, 982 KiB  
Article
Block Sparse Bayesian Learning Based Joint User Activity Detection and Channel Estimation in Grant-Free MIMO-NOMA
by Shuo Chen, Haojie Li, Lanjie Zhang, Mingyu Zhou and Xuehua Li
Drones 2023, 7(1), 27; https://doi.org/10.3390/drones7010027 - 31 Dec 2022
Cited by 2 | Viewed by 1602
Abstract
In the massive machine type of communication (mMTC), grant-free non-orthogonal multiple access (NOMA) is receiving more and more attention because it can skip the complex grant process to allocate non-orthogonal resources to serve more users. To address the limited wireless resources and substantial [...] Read more.
In the massive machine type of communication (mMTC), grant-free non-orthogonal multiple access (NOMA) is receiving more and more attention because it can skip the complex grant process to allocate non-orthogonal resources to serve more users. To address the limited wireless resources and substantial connection challenges, combining grant-free NOMA and multiple-input multiple-output (MIMO) is crucial to further improve the system’s capacity. In the grant-free MIMO-NOMA system, the base station should obtain the relevant information of the user before data detection. Thus, user activity detection (UAD) and channel estimation (CE) are two problems that should be solved urgently. In this paper, we fully consider the sparse characteristics of signals and the spatial correlation between multiple antennas in the grant-free MIMO-NOMA system. Then, we propose a spatial correlation block sparse Bayesian learning (SC-BSBL) algorithm to address the joint UAD and CE problems. First, by fully mining the block sparsity of signals in the grant-free MIMO-NOMA system, we model the joint UAD and CE problem as a three-dimensional block sparse signal recovery problem. Second, we derive the cost function based on the hierarchical Bayesian theory and spatial correlation. Finally, to estimate the channel and the set of active users, we optimize the cost function with fast marginal likelihood maximization. The simulation results indicate that, compared with the existing algorithms, SC-BSBL can always fully use the signal sparsity and spatial correlation to accurately complete UAD and CE under various user activation probabilities, SNRs, and the number of antennas. The normalized mean square error of CE can be reduced to 0.01, and the UAD error rate can be less than 10−5. Full article
(This article belongs to the Special Issue Recent Advances in Unmanned Aerial Vehicles for Next Generation Wireless Communications Networks)
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16 pages, 6205 KiB  
Article
Architecture of Emergency Communication Systems in Disasters through UAVs in 5G and Beyond
by A. F. M. Shahen Shah
Drones 2023, 7(1), 25; https://doi.org/10.3390/drones7010025 - 29 Dec 2022
Cited by 18 | Viewed by 5015
Abstract
Unmanned aerial vehicles (UAVs) are valued in 5G and 6G networks due to their communication capabilities, low cost, and flexible deployment. Recently, UAV-aided emergency networks in disasters have been designed where one single large UAV is used. Compared with a single large UAV, [...] Read more.
Unmanned aerial vehicles (UAVs) are valued in 5G and 6G networks due to their communication capabilities, low cost, and flexible deployment. Recently, UAV-aided emergency networks in disasters have been designed where one single large UAV is used. Compared with a single large UAV, Flying Ad Hoc Networks (FANETs) with small UAVs have many benefits. Therefore, instead of a single large UAV, a FANET is proposed in this paper. To take full advantage of their services, UAVs must be able to communicate efficiently with each other and with existing networking infrastructures. However, high node mobility is one of the main characteristics of FANETs, which can result in rapid topology changes with frequent link breakage and unstable communications that cause collision and packet loss. As an alternative, networks can be broken up into smaller groups or clusters to control their topology efficiently and reduce channel contention. In this study, a novel cluster-based mechanism is proposed for FANETs. The process of cluster management is described. The IEEE 802.11 backoff method is specifically intended for direct communications and is not appropriate for cluster-based communications. Therefore, a new backoff mechanism is proposed based on cluster size to optimize performance. An analytical study using the Markov chain model is presented to explore the performance of the proposed mechanism. The study takes into account Nakagami-m fading channels. Performance-influencing parameters are taken into consideration and the relationships among these parameters as well as performance metrics such as throughput, packet dropping rate, outage probability, and delay are obtained. Furthermore, simulation results are provided which verify the analytical studies. A quantitative comparison with current cluster-based methods is also presented. The simulation results show that the suggested technique enhances system performance and complies with the safety message delay constraint of 100 ms. Full article
(This article belongs to the Special Issue Recent Advances in Unmanned Aerial Vehicles for Next Generation Wireless Communications Networks)
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25 pages, 1028 KiB  
Article
Ergodic Performance Analysis of Double Intelligent Reflecting Surfaces-Aided NOMA–UAV Systems with Hardware Impairment
by Minh-Sang Van Nguyen, Dinh-Thuan Do, Van-Duc Phan, Wali Ullah Khan, Agbotiname Lucky Imoize and Mostafa M. Fouda
Drones 2022, 6(12), 408; https://doi.org/10.3390/drones6120408 - 12 Dec 2022
Cited by 10 | Viewed by 2222
Abstract
In this work, we design an intelligent reflecting surface (IRS)-assisted Internet of Things (IoT) by enabling non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAV) approaches. We pay attention to studying the achievable rates for the ground users. A practical system model takes [...] Read more.
In this work, we design an intelligent reflecting surface (IRS)-assisted Internet of Things (IoT) by enabling non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAV) approaches. We pay attention to studying the achievable rates for the ground users. A practical system model takes into account the presence of hardware impairment when Rayleigh and Rician channels are adopted for the IRS–NOMA–UAV system. Our main findings are presented to showcase the exact expressions for achievable rates, and then we derive their simple approximations for a more insightful performance evaluation. The validity of these approximations is demonstrated using extensive Monte Carlo simulations. We confirm the achievable rate improvement decided by main parameters such as the average signal to noise ratio at source, the position of IRS with respect to the source and destination and the number of IRS elements. As a suggestion for the deployment of a low-cost IoT system, the double-IRS model is a reliable approach to realizing the system as long as the hardware impairment level is controlled. The results show that the proposed scheme can greatly improve achievable rates, obtain optimal performance at one of two devices and exhibit a small performance gap compared with the other benchmark scheme. Full article
(This article belongs to the Special Issue Recent Advances in Unmanned Aerial Vehicles for Next Generation Wireless Communications Networks)
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18 pages, 1946 KiB  
Article
A UAV-Swarm-Communication Model Using a Machine-Learning Approach for Search-and-Rescue Applications
by Hisham Khalil, Saeed Ur Rahman, Inam Ullah, Inayat Khan, Abdulaziz Jarallah Alghadhban, Mosleh Hmoud Al-Adhaileh, Gauhar Ali and Mohammed ElAffendi
Drones 2022, 6(12), 372; https://doi.org/10.3390/drones6120372 - 23 Nov 2022
Cited by 29 | Viewed by 5532
Abstract
This paper presents a UAV-swarm-communication model using a machine-learning approach for search-and-rescue applications. Firstly, regarding the communication of UAVs, the receive signal strength (RSS) and power loss have been modeled using random forest regression, and the mathematical representation of the channel matrix has [...] Read more.
This paper presents a UAV-swarm-communication model using a machine-learning approach for search-and-rescue applications. Firstly, regarding the communication of UAVs, the receive signal strength (RSS) and power loss have been modeled using random forest regression, and the mathematical representation of the channel matrix has also been discussed. The second part consisted of swarm control modeling of UAVs; however, a dataset for five types of triangular swarm formations was generated, and K-means clustering was applied to predict the cluster. In order to obtain the correct swarm formation, the dendrogram of all types was investigated. Finally, the heat map and contour were plotted for all kinds of swarm clusters. Furthermore, it was observed that the RSS of proposed swarms had good agreement with swarm distances. Full article
(This article belongs to the Special Issue Recent Advances in Unmanned Aerial Vehicles for Next Generation Wireless Communications Networks)
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