Coverage Analysis for Low-Altitude UAV Networks in Urban Environments

Galkin, Boris; Kibiłda, Jacek; DaSilva, Luiz A.

doi:10.1109/glocom.2017.8254658

Cited by 94 publications

(85 citation statements)

References 13 publications

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“…The authors calculate optimal flying trajectories for a fixed‐wing drone that has to maintain communication with a stationary ground station. Galkin et al derive analytic formulas for an optimal altitude of a randomly moving network of drones to maximize the coverage probability for the stationary user.…”

Section: Planning Drone Operationsmentioning

confidence: 99%

“…Because data transmission and setup operations to establish links consume the energy of drones, a common objective function is to maximize energy efficiency or minimize energy to achieve the desired communication quality . Alternatively, the number of established communication links and the quality of communication , such as packet transmission delay or the coverage probability , should be optimized given limited resources, such as the limited energy of the drone.…”

Section: Planning Drone Operationsmentioning

confidence: 99%

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Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

et al. 2018

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Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult‐to‐access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.

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Section: Planning Drone Operationsmentioning

confidence: 99%

Section: Planning Drone Operationsmentioning

confidence: 99%

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

et al. 2018

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“…We assume that each UAV has the same type of directional antenna with beamwidth θ 0 , and the main beam covers the region directly beneath the UAV. This coverage cone has a radius 3 tan( θ 0 2 )H. The interference beyond this coverage is negligible [27], [29]. As show in Fig.…”

Section: Air-to-ground (A2g) Channel Modelmentioning

confidence: 99%

“…Update z a(l,t) := z a(l,t) + 1, ∀(l, t) ∈ V(s −m )\V(s m ); 10 Based on (30), update the vertex reward k (z k ), ∀k ∈ K; H Altitude of the UAV 90 m [9] θ0 Antenna beamwidth 2.7854 rad [27], [29] fc Carrier frequency 2 GHz [9] N0 Noise power −96 dBm [9] α Path-loss exponent 2 [38] η1, η2 Path-loss for LOS, NLOS 3, 23 dB [9], [38] ψ, ζ Environment parameters 11.95, 0.14 [38] Λ0, Λ1, Λ2 Rotary-wing UAV physical property 580.65, 790.67, 0.01 [20] ω Tip speed of the rotor blade 200 m/s [20] χ Mean rotor induced velocity in hovering (see Equation (2.12) in [39] and Equation (12.1) of [40]) 7.2 [20] V. PERFORMANCE EVALUATIONS…”

Section: B Distributed Route Selection Algorithmmentioning

confidence: 99%

Trajectory Design for UAV Assisted Wireless Networks

Tang

Cheung

Lok

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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Unmanned aerial vehicles (UAVs) can enhance the performance of cellular networks, due to their high mobility and efficient deployment. In this paper, we present a first study on how the user mobility affects the UAVs' trajectories of a multiple-UAV assisted wireless communication system. Specifically, we consider the UAVs are deployed as aerial base stations to serve ground users who move between different regions. We maximize the throughput of ground users in the downlink communication by optimizing the UAVs' trajectories, while taking into account the impact of the user mobility, propulsion energy consumption, and UAVs' mutual interference. We formulate the problem as a route selection problem in an acyclic directed graph. Each vertex represents a task associated with a reward on the average user throughput in a region-time point, while each edge is associated with a cost on the energy propulsion consumption during flying and hovering. For the centralized trajectory design, we first propose the shortest path scheme that determines the optimal trajectory for the single UAV case. We also propose the centralized route selection (CRS) scheme to systematically compute the optimal trajectories for the more general multiple-UAV case. Due to the NP-hardness of the centralized problem, we consider the distributed trajectory design that each UAV selects its trajectory autonomously. We formulate the UAVs' interactions as a route selection game. We prove that it is a potential game with the finite improvement property, which guarantees our proposed distributed route selection (DRS) scheme will converge to a pure strategy Nash equilibrium within a finite number of iterations. Simulation results show that our DRS scheme results in a near-optimal performance that achieves 95% of the maximal total payoff. Moreover, it achieves the highest average payoff and energy efficiency among the benchmark greedy path and circular path schemes. throughput within a given time length in [14]. Furthermore, the authors in [15] proposed a new cyclical multiple access scheme that the UAV flies cyclically above the ground, and characterized the max-min throughput by optimally allocating the transmission time to ground terminals based on the UAV position. In [16], a UAV was dispatched to disseminate a common file to a set of ground terminals (GTs) and the authors aimed to design the UAV trajectory to minimize its mission completion time, and [17] optimized the trajectory design to maximize the amount of energy transferred to all energy receivers during a finite charging period. The above studies mainly focus on communication performance improvement and the technical challenges that exist in trajectory design are energy limitation, interference mitigation, and user mobility. Since UAVs consume a significant amount of energy to support their mobility, it motivated the design of the energy-efficient UAV communication via trajectory design in [19]-[22]. The authors of [19] focused on the energy efficient maximization of a fixed-wing UAV enabled commu...

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“…The system-level analysis of UAV-assisted networks has also attracted much attention in recent literature. For instance, references [12], [13] and [15] considered a two dimensional (2D) PPP UAVassisted cellular network, where UAVs were distributed according to a PPP at the same height in the air.…”

Section: Introductionmentioning

confidence: 99%

Coverage Analysis for Energy-Harvesting UAV-Assisted mmWave Cellular Networks

Wang

Gursoy

2019

IEEE J. Select. Areas Commun.

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In this paper, we jointly consider the downlink simultaneous wireless information and power transfer (SWIPT) and uplink information transmission in unmanned aerial vehicle (UAV)-assisted millimeter wave (mmWave) cellular networks, in which the user equipment (UE) locations are modeled using Poisson cluster processes (e.g., Thomas cluster processes or Matérn cluster processes). Distinguishing features of mmWave communications, such as different path loss models for line-of-sight (LOS) and non-LOS (NLOS) links and directional transmissions are taken into account. In the downlink phase, the association probability, and energy coverages of different tier UAVs and ground base stations (GBSs) are investigated. Moreover, we define a successful transmission probability to jointly present the energy and signal-to-interference-plus-noise ratio (SINR) coverages and provide general expressions. In the uplink phase, we consider the scenario that each UAV receives information from its own cluster member UEs. We determine the Laplace transform of the interference components and characterize the uplink SINR coverage. In addition, we formulate the average uplink throughput, with the goal to identify the optimal time division multiplexing between the donwlink and uplink phases. Through numerical results we investigate the impact of key system parameters on the performance. We show that the network performance is improved when the cluster size becomes smaller. In addition, we analyze the optimal height of UAVs, optimal power splitting value and optimal time division multiplexing that maximizes the network performance.The authors are with the [7]. In conventional wireless power transfer (WPT) systems, energy transmitters are deployed at fixed locations, and therefore due to the RF signal propagation over potentially long distances, such systems can suffer from low end-to-end energy transfer efficiency [4]. In general, RF WPT is considered in the context of two key application scenarios, namely simultaneous wireless information and power transfer (SWIPT) and wireless powered communication networks (WPCNs). SWIPT explores the dual use of microwave signals to achieve WPT and wireless information transfer (WIT) both in downlink direction [8], while downlink WPT and uplink WIT are performed in WPCNs [9]. Unmanned aerial vehicles (UAVs) have emerged as key enablers of seamless wireless connectivity in diverse scenarios such as large-scale temporary events, military operations and disaster scenarios, and of capacity enhancement in the occasional demand of super dense base stations (BSs), and UAVs are anticipated to be part of future generation wireless networks [10][11][12][13]. More specifically, in order to take advantage of flexible deployment opportunities [12], and high possibility of line-of-sight (LoS) connections with a ground user equipment (UE) [14], BSs can be mounted on UAVs to support wireless connectivity and improve the performance of cellular networks [13]. The flexibility of UAV BSs allows them to adapt their locations ...

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Coverage Analysis for Low-Altitude UAV Networks in Urban Environments

Cited by 94 publications

References 13 publications

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Trajectory Design for UAV Assisted Wireless Networks

Coverage Analysis for Energy-Harvesting UAV-Assisted mmWave Cellular Networks

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