Performance Analysis of UAV Enabled Disaster Recovery Networks: A Stochastic Geometric Framework Based on Cluster Processes

Hayajneh, Ali; Zaidi, Syed Ali Raza; McLernon, Des; Renzo, Marco Di; Ghogho, Mounir

doi:10.1109/access.2018.2835638

Cited by 119 publications

(79 citation statements)

References 34 publications

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“…By reducing the distance to the GS, the ergodic rate of the FSO channel drastically increases, due to the exponential reduction of the atmospheric loss. On the other hand, the ergodic rate of the RF channel decreases due to the larger path loss and the smaller LOS probability caused by the smaller elevation angle in (3). Consequently, farther from the cell center, the RF channel is the performance bottleneck and the ergodic rates of both BA and non-BA relaying approach the ergodic sum rate of the RF channel.…”

Section: B Impact Of Positioning Of the Uavmentioning

confidence: 99%

“…They determined the optimal placement of a stationary UAV and the optimal trajectory of a moving UAV in terms of the maximum throughput. Furthermore, the performance of a cluster-based UAV network in terms of coverage probability and energy efficiency was analyzed in [3]. The authors of [14] investigated the optimal positioning in a multi-UAV network with the objective to minimize the total transmit power.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [14] investigated the optimal positioning in a multi-UAV network with the objective to minimize the total transmit power. In both [3] and [14], the backhaul link was assumed to be ideal. The authors of [15] considered the impact of the positioning of the UAV on both the access and the backhaul links.…”

Section: Introductionmentioning

confidence: 99%

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Ergodic Sum Rate Analysis of UAV-Based Relay Networks With Mixed RF-FSO Channels

Ajam

Najafi

Jamali

et al. 2020

IEEE Open J. Commun. Soc.

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Unmanned aerial vehicle (UAV)-based communications is a promising new technology that can add a wide range of new capabilities to the current network infrastructure. Given the flexibility, costefficiency, and convenient use of UAVs, they can be deployed as temporary base stations (BSs) for ondemand situations like BS overloading or natural disasters. In this work, a UAV-based communication system with radio frequency (RF) access links to the mobile users (MUs) and a free-space optical (FSO) backhaul link to the ground station (GS) is considered. In particular, the RF and FSO channels in this network depend on the UAV's positioning and (in)stability. The relative position of the UAV with respect to the MUs impacts the likelihood of a line-of-sight (LOS) connection in the RF link and the instability of the hovering UAV affects the quality of the FSO channel. Thus, taking these effects into account, we analyze the end-to-end system performance of networks employing UAVs as buffer-aided (BA) and non-buffer-aided (non-BA) relays in terms of the ergodic sum rate. Simulation results validate the accuracy of the proposed analytical derivations and reveal the benefits of buffering for compensation of the random fluctuations caused by the UAV's instability. Our simulations also show that the ergodic sum rate of both BA and non-BA UAV-based relays can be enhanced considerably by optimizing the positioning of the UAV. We further study the impact of the MU density and the weather conditions on the end-to-end system performance.

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Section: B Impact Of Positioning Of the Uavmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ergodic Sum Rate Analysis of UAV-Based Relay Networks With Mixed RF-FSO Channels

Ajam

Najafi

Jamali

et al. 2020

IEEE Open J. Commun. Soc.

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“…The largest received power association criterion we used here is different from the prior work on UAV-assisted cellular networks with PCP models (e.g., [29] [32]), since these studies adopted either the nearest association criterion or the random association criterion. And the largest received power association is more practical while being relatively more difficult to analyze.…”

Section: B Contributionsmentioning

confidence: 99%

“…PCP models have also been considered in the system-level analysis of UAV-assisted networks. In [29], the UAVs were assumed to form a PCP with the destroyed macro BSs as the parent nodes. The downlink network performance, i.e.…”

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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Altitude and density optimization over UAV‐enabled massive IoT wireless communications

Shabani

Faraji

Baghani

2022

Trans Emerging Tel Tech

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Utilizing unmanned aerial vehicles (UAVs) as aerial base stations is a new and promising technology that enables the connectivity of a large volume of devices such as sensors and machines, referred to as massive Internet of Things (mIoT). This article aims to analyze and optimize the area spectral efficiency (ASE) by investigating the efficient deployment of UAVs to ensure reliable uplink communication from ground IoT devices to UAVs. We utilize the tools from stochastic geometry to derive the closed form expression of the ASE in the interference-limited regime. We propose a novel framework for maximizing the ASE of UAV-enabled networks by simultaneous optimization of UAVs' altitude and density. The simulation results demonstrate that deploying UAVs with combined optimal density and altitude outperforms more conventional deployment strategies in terms of the ASE.

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Performance Analysis of UAV Enabled Disaster Recovery Networks: A Stochastic Geometric Framework Based on Cluster Processes

Cited by 119 publications

References 34 publications

Ergodic Sum Rate Analysis of UAV-Based Relay Networks With Mixed RF-FSO Channels

Ergodic Sum Rate Analysis of UAV-Based Relay Networks With Mixed RF-FSO Channels

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

Altitude and density optimization over UAV‐enabled massive IoT wireless communications

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