Cellular Connectivity for UAVs: Network Modeling, Performance Analysis, and Design Guidelines

Azari, Mohammad Mahdi; Rosas, Fernando; Pollin, Sofie

doi:10.1109/twc.2019.2910112

Cited by 163 publications

(133 citation statements)

References 34 publications

(53 reference statements)

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“…where (a) follows from the independence of the two point processes that represent the aerial-BSs and the terrestrial-BSs, (b) follows from using the definition of the Complementary CDF and averaging over R L . Finally, using (13) and (15) along with some mathematical manipulations complete the proof.…”

Section: Appendix B Proof Of Lemmamentioning

confidence: 92%

“…Due to the emerging applications of UAVs as aerial-BSs and aerial users (e.g., delivery-drones), the performance of the VHetNets that involve aerial users as well as terrestrial users received a great interest. The authors in [12] and [13] investigate the feasibility of serving aerial and terrestrial users using a terrestrial wireless network. The authors claim that the LoS conditions that the aerial users experience with the terrestrial-BSs degrade the SINR at the terrestrial users due to the strong interference signals received from the LoS aerial users.…”

Section: A Related Workmentioning

confidence: 99%

“…The authors claim that the LoS conditions that the aerial users experience with the terrestrial-BSs degrade the SINR at the terrestrial users due to the strong interference signals received from the LoS aerial users. Furthermore, the work in [12] and [13] present several tuning parameters to combat these strong interference signals. However, they do not consider the existence of aerial-BSs which could be a candidate to serve aerial users, especially if the terrestrial wireless network is congested.…”

Section: A Related Workmentioning

confidence: 99%

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Downlink Coverage Analysis of an Aerial User in Vertical Heterogeneous Networks

Cherif

Amroune²,

Yanıkömeroğlu³

et al. 2019

2019 IEEE Global Communications Conference (GLOBECOM)

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In this paper, the downlink coverage probability and average achievable rate of an aerial user in a vertical HetNet (VHetNet) comprising aerial base stations (aerial-BSs) and terrestrial-BSs are analyzed. The locations of terrestrial-BSs are modeled as an infinite 2-D Poisson point process (PPP) while the locations of aerial-BSs are modeled as a finite 3-D Binomial point process (BPP) deployed at a particular height. We adopt cellular-to-air (C2A) channel model that incorporates LoS and NLoS transmissions between the terrestrial-BSs and the typical aerial user while we assume LoS transmissions for the air-toair (A2A) channels separating the aerial user and aerial-BSs. For tractability reasons, we simplify the expression of the LoS probability provided by the International Telecommunications Union using curve fitting. We assume that the aerial user is associated with the BS (either an aerial-BS or terrestrial-BS) that provides the strongest average received power. Using tools from stochastic geometry, we derive analytical expressions of the coverage probability and achievable rate in terms of the Laplace transform of interference power. To simplify the derived analytical expressions, we assume that the C2A links are in LoS conditions. Although this approximation gives pessimistic results compared to the exact performance, the analytical approximations are easier to evaluate and quantify well the performance at high heights of the aerial user. Our findings reveal that using directive beamforming for the aerial-BSs improves the downlink performance substantially since it alleviates the strong interference signals received from the aerial-BSs.

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Section: Appendix B Proof Of Lemmamentioning

confidence: 92%

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

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Downlink Coverage Analysis of an Aerial User in Vertical Heterogeneous Networks

Cherif

Amroune²,

Yanıkömeroğlu³

et al. 2019

2019 IEEE Global Communications Conference (GLOBECOM)

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“…There are already a few prior works focusing on the performance analysis of UAV networks. The majority of them particularly studied the coverage performance of UAV networks with different constraints (typically see [7]- [14]). Reference [7], for instance, studied how to optimize the coverage of a single cell by effectively deploying multiple UAVs over the cell.…”

Section: A Motivation and Prior Workmentioning

confidence: 99%

MmWave UAV Networks With Multi-Cell Association: Performance Limit and Optimization

Liu

2019

IEEE J. Select. Areas Commun.

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This paper aims to exploit the fundamental limits on the downlink coverage and spatial throughput performances of a cellular network consisting of a tier of unmanned aerial vehicle (UAV) base stations (BSs) using the millimeter wave (mmWave) band and a tier of ground BSs using the ultra high frequency (UHF) band. To reduce handover signaling overhead, the ground BSs take charge of control signaling delivery whereas the UAVs are in charge of payload data transmission so that users need to simultaneously associate with a ground BS and a UAV in this network with a control-data plane-split architecture. We first propose a three-dimensional (3D) location distribution model of the UAVs using stochastic geometry which is able to generally characterize the positions of the UAVs in the sky. Using this 3D distribution model, we propose the multi-cell coverage probability and the volume spectral efficiency of the network, derive their explicit low-complexity expressions and find their upper limits when each of the UAVs and ground BSs is equipped with a massive antenna array. We further show that the multi-cell coverage probability and the volume spectral efficiency can be maximized by optimally deploying and positioning the UAVs in the sky and thereby their fundamental maximal limits are found. These important analytical findings are validated by numerical simulations.Index Terms-Unmanned aerial vehicle network, millimeter wave, coverage, throughput, cell association, stochastic geometry. arXiv:1905.06099v3 [cs.IT] 18 Jul 2019 by a clustered channel model consisting of small-scale fading and angle-ofdeparture (AoD)-based transmit array gain vectors. Here we thus assume that all UAVs have a uniform linear array and are able to perfectly align their beam with the angle-of-departure (AoD) of their array in order to maximize their antenna array gain from them to their serving users.

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“…Moreover, these works only considered scenarios of static UAV-UEs. Furthermore, while the authors in [25]- [27] studied the performance of mobile UAV-UEs, their results were based on system simulations and measurement trials.…”

mentioning

confidence: 99%

Mobility in the Sky: Performance and Mobility Analysis for Cellular-Connected UAVs

Amer

Saad

Marchetti

2020

IEEE Trans. Commun.

128

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Providing connectivity to unmanned aerial vehicle-user equipments (UAV-UEs), such as drones or flying taxis, is a major challenge for tomorrow's cellular systems. In this paper, the use of coordinated multi-point (CoMP) transmission for providing seamless connectivity to UAV-UEs is investigated. In particular, a network of clustered ground base stations (BSs) that cooperatively serve a number of UAV-UEs is considered. Two scenarios are studied: scenarios with static, hovering UAV-UEs and scenarios with mobile UAV-UEs. Under a maximum ratio transmission, a novel framework is developed and leveraged to derive upper and lower bounds on the UAV-UE coverage probability for both scenarios.Using the derived results, the effects of various system parameters such as collaboration distance, UAV-UE altitude, and UAV-UE velocity on the achievable performance are studied. Results reveal that, for both static and mobile UAV-UEs, when the BS antennas are tilted downwards, the coverage probability of a high-altitude UAV-UE is upper bounded by that of ground users regardless of the transmission scheme. Moreover, for low signal-to-interference-ratio thresholds, it is shown that CoMP transmission can improve the coverage probability of UAV-UEs, e.g., from 28% under the nearest association scheme to 60% for a collaboration distance of 200 m. Meanwhile, key results on mobile UAV-UEs unveil that not only the spatial displacements of UAV-UEs but also their vertical motions affect their handover rate and coverage probability. In particular, UAV-UEs that have frequent vertical movements and high direction switch rates are expected to have low handover probability and handover rate. Finally, the effect of the UAV-UE vertical movements on its coverage probability is marginal if the UAV-UE retains the same mean altitude.The past few years have witnessed a tremendous increase in the use of unmanned aerial vehicles (UAVs), popularly called drones, in many applications, such as aerial surveillance, package delivery, and even flying taxis [2]- [6]. Enabling such UAV-centric applications requires ubiquitous wireless connectivity that can be potentially provided by the pervasive wireless cellular network [7] and [8]. However, in order to operate cellular-connected UAVs using existing wireless systems, one must address a broad range of challenges that include interference mitigation, reliable communications, resource allocation, and mobility support [9]. Next, we review some of the works relevant to the cellular-connected UAV-enabled networks. A. State of the Art and Prior WorksRecently, cellular-connected UAVs have received significant attention, whereby UAVs as new user equipments (UEs) are integrated into the cellular network in order to ensure reliable and secure connectivity for the operations of UAV systems. However, it has been established that the dominance of line-of-sight (LoS) links makes inter-cell interference a critical issue for cellular systems with hybrid terrestrial and aerial UEs. In this regard, extensive real-world simulations...

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Cellular Connectivity for UAVs: Network Modeling, Performance Analysis, and Design Guidelines

Cited by 163 publications

References 34 publications

Downlink Coverage Analysis of an Aerial User in Vertical Heterogeneous Networks

Downlink Coverage Analysis of an Aerial User in Vertical Heterogeneous Networks

MmWave UAV Networks With Multi-Cell Association: Performance Limit and Optimization

Mobility in the Sky: Performance and Mobility Analysis for Cellular-Connected UAVs

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