Deployment of UAV-mounted access points according to spatial user locations in two-tier cellular networks

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

doi:10.1109/wd.2016.7461487

Cited by 132 publications

(90 citation statements)

References 14 publications

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“…K-means clustering is a heuristic algorithm which partitions a given set of points in space into cells of approximately equal size and finds the centroid of the cells. In our previous work [17] we have demonstrated how this algorithm can be used to optimally position UAVs around known user locations. For the low UAV density scenario in Fig.…”

Section: B Uav Placement Comparisonmentioning

confidence: 99%

A Stochastic Model for UAV Networks Positioned Above Demand Hotspots in Urban Environments

Galkin

Kibiłda

DaSilva

2019

IEEE Trans. Veh. Technol.

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Wireless access points on unmanned aerial vehicles (UAVs) are being considered for mobile service provisioning in commercial networks. To be able to efficiently use these devices in cellular networks it is necessary to first have a qualitative and quantitative understanding of how their design parameters reflect on the service quality experienced by the end user. In this paper we use stochastic geometry to characterise the behaviour of a network of UAV access points that intelligently position themselves above user hotspots, and we evaluate the performance of such a network against cases where the UAVs are positioned in a rectangular grid or according to heuristic positioning algorithms.

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Section: B Uav Placement Comparisonmentioning

confidence: 99%

A Stochastic Model for UAV Networks Positioned Above Demand Hotspots in Urban Environments

Galkin

Kibiłda

DaSilva

2019

IEEE Trans. Veh. Technol.

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“…To obtain the numerical results at the medium height, we choose to analyze UAVs at the height of 50m and 100m, which are the most practical cases in reality. For the high-altitude model, the relative parameters are: A L = 10.38, A NL = 14.54, α L = 2.09, α NL = 3.75 [4], [13]. For the low-altitude model, path loss parameters are: A L = 10.34, A NL = 13.11, α L = 2.42, α NL = 4.28 [11].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Positions of UAV-BSs were modeled as a 3D Poisson Point Process (3D-PPP) distribution with a limited height in [1], but the analysis in [3] showed that the flexible height of UAV is not as helpful as a well-chosen fixed altitude. In [4], UAV-mounted mobile base stations were The work of Y.-C. Liang is funded by National Natural Science Foundation of China under Grants 61571100, 61631005 and 61628103. deployed in a fixed altitude and placed along an optimal trajectory to cover as much as user equipment (UE) whose locations are already known in a given area.…”

Section: Introductionmentioning

confidence: 99%

“…Although the path loss model has been considered as a key factor in the performance analysis for UAV networks, there is no consensus on this issue yet. For example, the work in [1] and [4] only considered the UAV hovering in a LoS dominated network for simplicity. To conduct a practical analysis for UAV, the authors of [8] proposed a general path loss model which considers both LoS and NLoS connections and their occurrence probabilities, depending on the elevation angle between a UAV and a user.…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis for Practical Unmanned Aerial Vehicle Networks with LoS/NLoS Transmissions

Liu

Ding

et al. 2018

2018 IEEE International Conference on Communications Workshops (ICC Workshops)

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In this paper, we provide a performance analysis for practical unmanned aerial vehicle (UAV)-enabled networks. By considering both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions between aerial base stations (BSs) and ground users, the coverage probability and the area spectral efficiency (ASE) are derived. Considering that there is no consensus on the path loss model for studying UAVs in the literature, in this paper, three path loss models, i.e., high-altitude model, low-altitude model and ultra-low-altitude model, are investigated and compared. Moreover, the lower bound of the network performance is obtained assuming that UAVs are hovering randomly according to homogeneous Poisson point process (HPPP), while the upper bound is derived assuming that UAVs can instantaneously move to the positions directly overhead ground users. From our analytical and simulation results for a practical UAV height of 50 meters, we find that the network performance of the high-altitude model and the low-altitude model exhibit similar trends, while that of the ultra-low-altitude model deviates significantly from the above two models. In addition, the optimal density of UAVs to maximize the coverage probability performance has also been investigated.

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“…al., [78] and Galkin et. al., [79], but instead of brute force search, they employed genetic algorithm and K-means clustering, respectively, to solve the optimization problem.…”

Section: A Placement Optimization For Aerial Bssmentioning

confidence: 99%

Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges

Fotouhi

Qiang

Ding

et al. 2019

IEEE Commun. Surv. Tutorials

858

485

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The rapid growth of consumer Unmanned Aerial Vehicles (UAVs) is creating promising new business opportunities for cellular operators. On the one hand, UAVs can be connected to cellular networks as new types of user equipment, therefore generating significant revenues for the operators that can guarantee their stringent service requirements. On the other hand, UAVs offer the unprecedented opportunity to realize UAVmounted flying base stations that can dynamically reposition themselves to boost coverage, spectral efficiency, and user quality of experience. Indeed, the standardization bodies are currently exploring possibilities for serving commercial UAVs with cellular networks. Industries are beginning to trial early prototypes of flying base stations or user equipments, while academia is in full swing researching mathematical and algorithmic solutions to address interesting new problems arising from flying nodes in cellular networks. In this article, we provide a comprehensive survey of all of these developments promoting smooth integration of UAVs into cellular networks. Specifically, we survey (i) the types of consumer UAVs currently available off-the-shelf, (ii) the interference issues and potential solutions addressed by standardization bodies for serving aerial users with the existing terrestrial base stations, (iii) the challenges and opportunities for assisting cellular communications with UAV-based flying relays and base stations, (iv) the ongoing prototyping and test bed activities, (v) the new regulations being developed to manage the commercial use of UAVs, and (vi) the cyber-physical security of UAV-assisted cellular communications.

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Deployment of UAV-mounted access points according to spatial user locations in two-tier cellular networks

Cited by 132 publications

References 14 publications

A Stochastic Model for UAV Networks Positioned Above Demand Hotspots in Urban Environments

A Stochastic Model for UAV Networks Positioned Above Demand Hotspots in Urban Environments

Performance Analysis for Practical Unmanned Aerial Vehicle Networks with LoS/NLoS Transmissions

Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges

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