Optimal Control of Telecommunication Aeroplatform in the Area of Emergency

Lysenko, Oleksandr; Valuiskyi, Stanislav; Kirchu, Pavel; Romaniuk, Anton

doi:10.20535/2411-2976.12013.14-20

Cited by 10 publications

(10 citation statements)

References 3 publications

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“…We assume that each drone-BS adopts a frequency division multiple access (FDMA) technique (as done in [19] and [28]) when servicing its associated drone-UEs. Hence, the average downlink 1 Note that, drone-BSs are an essential part of a 3D cellular network, since drone-UEs are not capable of continuously maintaining LoS links and sending uplink data directly to HAPs due to their mobility and energy limitations.…”

Section: System Modelmentioning

confidence: 99%

See 1 more Smart Citation

Beyond 5G With UAVs: Foundations of a 3D Wireless Cellular Network

Mozaffari

Kasgari

Saad

et al. 2019

IEEE Trans. Wireless Commun.

402

231

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In this paper, a novel concept of three-dimensional (3D) cellular networks, that integrate drone base stations (drone-BS) and cellular-connected drone users (drone-UEs), is introduced. For this new 3D cellular architecture, a novel framework for network planning for drone-BSs as well as latency-minimal cell association for drone-UEs is proposed. For network planning, a tractable method for drone-BSs' deployment based on the notion of truncated octahedron shapes is proposed that ensures full coverage for a given space with minimum number of drone-BSs. In addition, to characterize frequency planning in such 3D wireless networks, an analytical expression for the feasible integer frequency reuse factors is derived. Subsequently, an optimal 3D cell association scheme is developed for which the drone-UEs' latency, considering transmission, computation, and backhaul delays, is minimized. To this end, first, the spatial distribution of the drone-UEs is estimated using a kernel density estimation method, and the parameters of the estimator are obtained using a cross-validation method. Then, according to the spatial distribution of drone-UEs and the locations of drone-BSs, the latency-minimal 3D cell association for drone-UEs is derived by exploiting tools from optimal transport theory. Simulation results show that the proposed approach reduces the latency of drone-UEs compared to the classical cell association approach that uses a signal-to-interference-plus-noise ratio (SINR) criterion. In particular, the proposed approach yields a reduction of up to 46% in the average latency compared to the SINR-based association. The A preliminary conference version of this work appears in [1].Recent reports from the federal aviation administration (FAA) shows that the number of unmanned aerial vehicles (UAVs), also known as drones, will exceed 7 million in 2020 [2]. Such a massive use of drones will have significant impacts on wireless networking. From a wireless perspective, the two key roles of drones include: aerial base station (BS), and user equipment (UE) [3]-[5]. Due to their flexibility and inherent ability for line-of-sight (LoS) communications, drone-BSs can provide broadband, wide-scale, and reliable wireless connectivity during disasters and temporary events [4]-[11]. Moreover, drone-BSs offer a promising solution for ultra-flexible deployment and cost-effective wireless services, without the prohibitive costs of terrestrial BSs.Meanwhile, drones can also act as UEs (i.e., cellular-connected drone-UEs) that must connect to a wireless network so as to operate. In particular, cellular-connected drone-UEs can be used for wide range of applications such as package delivery [12], surveillance, remote sensing, and virtual reality. The key feature of drone-UEs is their ability to intelligently move in three dimensions and optimize their trajectory in order to efficiently complete their missions. Therefore, drone-UEs are widely used for delivery purposes such as in Amazon's prime air drone delivery service and drug delivery in me...

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Section: System Modelmentioning

confidence: 99%

“…Initialize: H ← set of candidate for {hx, hy, hz}, L(hbest) ← ∞ for hx, hy, hz ∈ H do for j = 1, · · · , L do Build a model using (28) with Xi, i ∈ {1, · · · , L}, i = j sum← sum+ 1 2 log hx + 1 2 log hy Fig. 9 shows the negative log-likelihood function.…”

Section: Algorithm 1 Drone-ues' Distribution Estimation Algorithmmentioning

confidence: 99%

Beyond 5G With UAVs: Foundations of a 3D Wireless Cellular Network

Mozaffari

Kasgari

Saad

et al. 2019

IEEE Trans. Wireless Commun.

402

231

View full text Add to dashboard Cite

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“…Let s i = (x i , y i , h i ) be the three-dimensional (3D) coordinate of each UAV i ∈ M with h i being the altitude of UAV i. We consider a downlink scenario in which each UAV adopts a frequency division multiple access (FDMA) technique to provide service for the ground users as done in [2] and [23]. Let P i and B i be, respectively, the maximum transmit power and the total available bandwidth for UAV i.…”

Section: System Modelmentioning

confidence: 99%

“…Proof: We use the Kantorovich duality theorem (23) in which f (x, y) and Γ = i∈M ω i δ s i are two probability measures, and J(v, s) is the cost function. Clearly, due to the continuity of f (x, y) and J(v, s i ), the Monge's problem is equivalent to the Monge-Kantorovich problem.…”

Section: B Optimal Cell Partitioningmentioning

confidence: 99%

Wireless Communication Using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization

Mozaffari

Saad

Bennis

et al. 2017

IEEE Trans. Wireless Commun.

336

203

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In this paper, the effective use of flight-time constrained unmanned aerial vehicles (UAVs) as flying base stations that can provide wireless service to ground users is investigated. In particular, a novel framework for optimizing the performance of such UAV-based wireless systems in terms of the average number of bits (data service) transmitted to users as well as UAVs' hover duration (i.e. flight time)is proposed. In the considered model, UAVs hover over a given geographical area to serve ground users that are distributed within the area based on an arbitrary spatial distribution function. In this case, two practical scenarios are considered. In the first scenario, based on the maximum possible hover times of UAVs, the average data service delivered to the users under a fair resource allocation scheme is maximized by finding the optimal cell partitions associated to the UAVs. Using the powerful mathematical framework of optimal transport theory, this cell partitioning problem is proved to be equivalent to a convex optimization problem. Subsequently, a gradient-based algorithm is proposed for optimally partitioning the geographical area based on the users' distribution, hover times, and locations of the UAVs. In the second scenario, given the load requirements of ground users, the minimum average hover time that the UAVs need for completely servicing their ground users is derived. To this end, first, an optimal bandwidth allocation scheme for serving the users is proposed. Then, given this optimal bandwidth allocation, the optimal cell partitions associated with the UAVs are derived by exploiting the optimal transport theory. Simulation results show that our proposed cell partitioning approach leads to a significantly higher fairness among the users compared to the classical weighted Voronoi diagram.Furthermore, the results demonstrate that the average hover time of the UAVs can be reduced by 64% by adopting the proposed optimal bandwidth allocation as well as the optimal cell partitioning approach.In addition, our results reveal an inherent tradeoff between the hover time of UAVs and bandwidth efficiency while serving the ground users. arXiv:1704.04813v1 [cs.IT] 16 Apr 2017 2 I. INTRODUCTION Recently, the use of aerial platforms such as unmanned aerial vehicles (UAVs), drones, balloons, and helikite has emerged as a promising solution for providing reliable and cost-effective wireless communication services for ground wireless devices [1]-[4]. In particular, UAVs can be deployed as flying base stations for coverage expansion and capacity enhancement of terrestrial cellular networks [2]-[10]. With their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs have several key potential applications in wireless systems. For instance, UAVs can be deployed to complement existing cellular systems by providing additional capacity to hotspot areas during temporary events. Moreover, UAVs can also be used to provide network coverage in emergency and public safety situations during which the existin...

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“…The transmission time ttransj consists of the following intervals: establishment of connection tvsvj (exchange of service messages), waiting for transmission tпрj (according to the protocol of multiple access to the channel) and transmission of monitoring data tbperj (the transmission rate s in the radio channel depends on the distance d between the node and the TA, protocols (pkf ) channel and physical layers). The data exchange time must exceed the lifetime of the radio channel during the flight of the TA over this node (12). Therefore, the flight altitude of a TA is determined taking into account the target control functions and restrictions on its resources.…”

Section: Fig 2 -Components Of the Ekahau Positioning Systemmentioning

confidence: 99%

Development of Methods of Positioning, Localization and Data Collection From Nodes of a Free Mobile Sensor Network Using Intelligent Adaptive Telecommunication Aeroplatforms

Romaniuk

Lysenko

Novikov

et al. 2021

ITS

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Background. The article presents the results of a study of methods of positioning, localization and data collection from nodes of a mobile wireless sensor network using intelligent adaptive telecommunication air platforms. To implement the study of this research topic, an analysis of literary sources on this topic was carried out. Based on a fairly rich bibliographic material, this work has the main task of examining, analyzing and systematizing already known approaches to positioning objects in wireless sensor networks using intelligent adaptive telecommunication air platforms and suggesting options for their development. Objective. The aim of the work is to improve the methods of direct data collection of TA from the nodes of BSM, the general directions of synthesis of which are defined in the work. Methods. Methods of cluster analysis (network clustering), graph theory (research of analytical models of BSM with TA functioning, construction of cluster topology), theory of telecommunication networks (when calculating bandwidth in BSM with TA radio channels) and theory were used to solve the formulated problem. (when developing a positioning model for telecommunications air platforms) Results. A technique for evaluating the effectiveness of methods for collecting data from wireless sensor networks using intelligent adaptive telecommunication air platforms is proposed. Conclusions. The method of collecting TA monitoring data from the main nodes of clustered BSM has been improved. The method of estimation of efficiency of methods of data collection with BSM by telecommunication air platforms is offered.

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Optimal Control of Telecommunication Aeroplatform in the Area of Emergency

Cited by 10 publications

References 3 publications

Beyond 5G With UAVs: Foundations of a 3D Wireless Cellular Network

Beyond 5G With UAVs: Foundations of a 3D Wireless Cellular Network

Wireless Communication Using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization

Development of Methods of Positioning, Localization and Data Collection From Nodes of a Free Mobile Sensor Network Using Intelligent Adaptive Telecommunication Aeroplatforms

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