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Applied Sciences
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UAV Assisted Wireless Communications
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UAV Assisted Wireless Communications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (15 September 2018) | Viewed by 26895

Special Issue Editors

Assoc. Prof. Dr. Md. Jahangir Hossain

E-Mail Website
Guest Editor
Faculty of Applied Science, School of Engineering, The University of British Columbia, Vancouver, BC, Canada
Interests: cooperative communication systems; cognitive radio systems; hierarchical and multidimensional modulations; bit-interleaved-coded modulation
Dr. Mahdi Ben Ghorbel

E-Mail Website
Guest Editor
School of Engineering, The University of British Columbia, Vancouver, BC, Canada
Interests: cognitive radio systems; dynamic spectrum allocation; energy efficienct communication networks; resource management and optimization

Special Issue Information

Dear Colleagues,

Unmanned Aerial Vehicles (UAV), also known as drones, have seen recently an increasing interest in a wide range of domains due to the numerous advantages they present, in terms of mobility, flexibility, and easy deployment.

With the challenges faced by communication networks to handle the growing demand and various services, UAVs represent an interesting solution to many problems, ranging from ensuring coverage in emergency situations and rural areas to network densification for highly dense areas. However, to be deployed, several challenges need to be addressed, such as limited energy, backhaul link, mobility and handover, etc.

The scope of this Special Issue is to address the potential research areas in UAV-assisted communications. We seek the submission of high-quality, original and unpublished manuscripts on topics including, but not limited to:

  • Channel modeling for air-to-ground and air-to-air communication
  • Channel reliability for drone based communication
  • 5G communication for UAV
  • Physical layer design for drone based communication
  • Power consumption and energy harvesting models
  • Positioning optimization
  • Information and communication reliability
  • Mobility impacts at different flying altitudes
  • Models and algorithms for control of UAV networks
  • UAV traffic management
  • Test bed results for UAV communication
  • Regulatory Issues
Assoc. Prof. Dr. Md. Jahangir Hossain
Dr. Mahdi Ben Ghorbel
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • Unmanned Aerial Vehicles
  • UAV-based communications
  • Vehicular communications

Published Papers (6 papers)

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Research

15 pages, 6043 KiB  
Article
Application of Auxiliary Classifier Wasserstein Generative Adversarial Networks in Wireless Signal Classification of Illegal Unmanned Aerial Vehicles
by Caidan Zhao, Caiyun Chen, Zeping He and Zhiqiang Wu
Appl. Sci. 2018, 8(12), 2664; https://doi.org/10.3390/app8122664 - 18 Dec 2018
Cited by 8 | Viewed by 4049
Abstract
Recently, many studies have reported on image synthesis based on Generative Adversarial Networks (GAN). However, the use of GAN does not provide much attention on the signal classification problem. In the context of using wireless signals to classify illegal Unmanned Aerial Vehicles (UAVs), [...] Read more.
Recently, many studies have reported on image synthesis based on Generative Adversarial Networks (GAN). However, the use of GAN does not provide much attention on the signal classification problem. In the context of using wireless signals to classify illegal Unmanned Aerial Vehicles (UAVs), this paper explores the feasibility of using GAN to improve the training datasets and obtain a better classification model, thereby improving the accuracy of classification. First, we use the generative model of GAN to generate a large datasets, which does not need manual annotation. At the same time, the discriminative model of GAN is improved to classify the types of signals based on the loss function of the discriminative model. Finally, this model can be used to the outdoor environment and obtain a real-time illegal UAVs signal classification system. Our experiments confirmed that the improvements on the Auxiliary Classifier Generative Adversarial Networks (AC-GANs) by limited datasets achieve excellent results. The recognition rate can reach more than 95% in the indoor environment, and this method is also applicable in the outdoor environment. Moreover, based on the theory of Wasserstein GANs (WGAN) and AC-GANs, a more robust Auxiliary Classifier Wasserstein GANs (AC-WGANs) model is obtained, which is suitable for multi-class UAVs. Through the combination of AC-WGANs and Universal Software Radio Peripheral (USRP) B210 software defined radio (SDR) platform, a real-time UAVs signal classification system is also implemented. Full article
(This article belongs to the Special Issue UAV Assisted Wireless Communications)
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16 pages, 2063 KiB  
Article
UAV Motion Strategies in Uncertain Dynamic Environments: A Path Planning Method Based on Q-Learning Strategy
by Jun-hui Cui, Rui-xuan Wei, Zong-cheng Liu and Kai Zhou
Appl. Sci. 2018, 8(11), 2169; https://doi.org/10.3390/app8112169 - 06 Nov 2018
Cited by 20 | Viewed by 3497
Abstract
A solution framework for UAV motion strategies in uncertain dynamic environments is constructed in this paper. Considering that the motion states of UAV might be influenced by some dynamic uncertainties, such as control strategies, flight environments, and any other bursting-out threats, we model [...] Read more.
A solution framework for UAV motion strategies in uncertain dynamic environments is constructed in this paper. Considering that the motion states of UAV might be influenced by some dynamic uncertainties, such as control strategies, flight environments, and any other bursting-out threats, we model the uncertain factors that might cause such influences to the path planning of the UAV, unified as an unobservable part of the system and take the acceleration together with the bank angle of the UAV as a control variable. Meanwhile, the cost function is chosen based on the tracking error, then the control instructions and flight path for UAV can be achieved. Then, the cost function can be optimized through Q-learning, and the best UAV action sequence for conflict avoidance under the moving threat environment can be obtained. According to Bellman’s optimization principle, the optimal action strategies can be obtained from the current confidence level. The method in this paper is more in line with the actual UAV path planning, since the generation of the path planning strategy at each moment takes into account the influence of the UAV control strategy on its motion at the next moment. The simulation results show that all the planning paths that are created according to the solution framework proposed in this paper have a very high tracking accuracy, and this method has a much shorter processing time as well as a shorter path it can create. Full article
(This article belongs to the Special Issue UAV Assisted Wireless Communications)
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24 pages, 3976 KiB  
Article
Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
by Woonghee Lee, Joon Yeop Lee, Jiyeon Lee, Kangho Kim, Seungho Yoo, Seongjoon Park and Hwangnam Kim
Appl. Sci. 2018, 8(11), 2027; https://doi.org/10.3390/app8112027 - 23 Oct 2018
Cited by 14 | Viewed by 6143
Abstract
Various unmanned aerial vehicles (UAVs), also called drones, have developed based on advances in hardware and software technologies. Thus, service providers in diverse areas have tried to utilize drones to create more effective solutions. In many cases, employing multiple drones is more effective [...] Read more.
Various unmanned aerial vehicles (UAVs), also called drones, have developed based on advances in hardware and software technologies. Thus, service providers in diverse areas have tried to utilize drones to create more effective solutions. In many cases, employing multiple drones is more effective to perform the given mission than using a single drone. To utilize multiple drones, the drones should be strongly connected, but it is not trivial to construct reliable and efficient networks for drones due to their high mobility. Therefore, we propose a ground control system (GCS) routing protocol (GCS-routing) to overcome this limitation and provide reliable and efficient multi-drone control system, where GCS-routing maximizes GCS utilization. GCS is the essential component of flying ad-hoc network (FANET) and can obtain information about drones. Using this information, GCS-routing can provide more effective routing, predict any topology changes, and react immediately. GCS-routing does not issue any periodic HELLO message for neighbor discovery or link cost estimation, which significantly enhances network performance. We implemented GCS-routing on real drones, and applied GCS-routing to actual drone fleets, as well as simulations to evaluate GCS-routing performance. The results clearly identify the advantages of the proposed routing protocol for drone networks compared with current routing protocols. Full article
(This article belongs to the Special Issue UAV Assisted Wireless Communications)
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20 pages, 4170 KiB  
Article
Formation Control Algorithm of Multi-UAV-Based Network Infrastructure
by Seongjoon Park, Kangho Kim, Hyunsoon Kim and Hwangnam Kim
Appl. Sci. 2018, 8(10), 1740; https://doi.org/10.3390/app8101740 - 27 Sep 2018
Cited by 25 | Viewed by 4625
Abstract
This paper addresses the analysis and the deployment of the network infrastructure based on multiple Unmanned Air Vehicles (UAVs). Despite the unprecedented potential to the mobility of the network infrastructure, there has been no effort to establish a mathematical model of the infrastructure [...] Read more.
This paper addresses the analysis and the deployment of the network infrastructure based on multiple Unmanned Air Vehicles (UAVs). Despite the unprecedented potential to the mobility of the network infrastructure, there has been no effort to establish a mathematical model of the infrastructure and formation control strategies. We model the generic dynamics of the network infrastructure and derive the network throughput of the infrastructure. Through the parametrization of the model, we extract the generic factors of the network protocols and verify our model through the Network Simulator 3 (ns-3). By exploiting our network analysis model, we propose a novel formation control algorithm that determines the location of the UAVs to maximize the efficiency of the network. To achieve the objectives of the infrastructure, we define the formation-shaping effect as forces and elaborately design them using the generic factors. The formation algorithm continuously approaches to the optimized formation of a fleet of UAVs to enhance the overall throughput of the terrestrial devices. Our evaluations show that the algorithm guarantees remarkably higher throughput than the static formations. Through the dynamic transformation of the UAV formation, we believe that the multi-UAV-based network infrastructure could expand the boundary of the existing infrastructure while reducing the network traffic. Full article
(This article belongs to the Special Issue UAV Assisted Wireless Communications)
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31 pages, 24480 KiB  
Article
Co-Optimization of Communication and Sensing for Multiple Unmanned Aerial Vehicles in Cooperative Target Tracking
by Zhong Liu, Xiaowei Fu and Xiaoguang Gao
Appl. Sci. 2018, 8(6), 899; https://doi.org/10.3390/app8060899 - 30 May 2018
Cited by 7 | Viewed by 3211
Abstract
In this paper, we consider motion-planning for multiple unmanned aerial vehicles (UAVs) that oversee cooperative target tracking in realistic communication environments. We present a novel multi-UAVs cooperative target tracking algorithm based on co-optimization of communication and sensing strategy, which can generate information-gathering trajectories [...] Read more.
In this paper, we consider motion-planning for multiple unmanned aerial vehicles (UAVs) that oversee cooperative target tracking in realistic communication environments. We present a novel multi-UAVs cooperative target tracking algorithm based on co-optimization of communication and sensing strategy, which can generate information-gathering trajectories considering the multi-hops communication reliability. Firstly, a packet-erasure channel model is used to describe the realistic wireless communication links, in which the probability of a successful information transmission is modeled as a function of the signal-to-noise ratio (SNR). Secondly, the Fisher information matrix (FIM) is used to quantify the information gained in target tracking. Thirdly, a scalar metric is used for trajectories panning over a finite time horizon. This scalar metric is a utility function of the expected information gain and the probability of a successful information transmission. With the combining of the sensing and communication into a utility function, the co-optimization of communication and sensing is reflected in the tradeoffs between maximizing information gained and improving communication reliability. The results of comparison simulations show that the proposed algorithm effectively improved estimation performance compared to the method that does not consider communication reliability. Full article
(This article belongs to the Special Issue UAV Assisted Wireless Communications)
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18 pages, 3374 KiB  
Article
Multi-UAVs Communication-Aware Cooperative Target Tracking
by Xiaowei Fu, Kunpeng Liu and Xiaoguang Gao
Appl. Sci. 2018, 8(6), 870; https://doi.org/10.3390/app8060870 - 25 May 2018
Cited by 15 | Viewed by 3862
Abstract
A kind of communication-aware cooperative target tracking algorithm is proposed, which is based on information consensus under multi-Unmanned Aerial Vehicles (UAVs) communication noise. Each UAV uses the extended Kalman filter to predict target movement and get an estimation of target state. The communication [...] Read more.
A kind of communication-aware cooperative target tracking algorithm is proposed, which is based on information consensus under multi-Unmanned Aerial Vehicles (UAVs) communication noise. Each UAV uses the extended Kalman filter to predict target movement and get an estimation of target state. The communication between UAVs is modeled as a signal to noise ratio model. During the information fusion process, communication noise is treated as a kind of observation noise, which makes UAVs reach a compromise between observation and communication. The classical consensus algorithm is used to deal with observed information, and consistency prediction of each UAV’s target state is obtained. Each UAV calculates its control inputs using receding horizon optimization method based on consistency results. The simulation results show that introducing communication noise can make UAVs more focused on maintaining good communication with other UAVs in the process of target tracking, and improve the accuracy of cooperative target tracking. Full article
(This article belongs to the Special Issue UAV Assisted Wireless Communications)
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