Performance Analysis of Mobile Cellular-Connected Drones under Practical Antenna Configurations

Amer, Ramy; Saad, Walid; Galkin, Boris; Marchetti, Nicola

doi:10.1109/icc40277.2020.9148841

Cited by 39 publications

(30 citation statements)

References 18 publications

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“…Lemma 1: The in-phase/quadrature (I/Q) decomposition of the composite received signal R(t) is represented as R = Z + Y + N . The IQ components of the AWGN, N I and N Q (such that N = N I + N Q ) are i.i.d Gaussian random variables, each distributed as ∼ N (0, N 0 /2) [31], [32], and the IQ-decomposition for the aggregate interference is given in (20), where ρ i,u = T 0 g u (t − τ i )g u (t) dt is the crosscorrelation between the transmit and receive waveforms for the i-th nearest UE, and {ρ i,u } is a sequence of i.i.d random variables with ρ i,u ∼ U[0, 1]. Similarly, I/Q decomposition of the SOI is given in (21), where η = η NLoS /η LoS , and…”

Section: Drone Detection In Rf Sensing Networkmentioning

confidence: 99%

“…While the authors in [19], study the impact of a mixed LoS/NLoS A2G channel on the performance of a droneassisted heterogeneous networks, possible impacts of 3D antenna patterns were ignored. Some other recent papers [20], [21] considered dedicated ground BSs serving a network of drones under practical 3D antenna patterns. While the cellular coverage performance of the network was studied in these recent works, passive sensing performance of drones has not been taken into account.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, Y I |V and Y Q |V are i.i.d random variables withY n |V ∼ N 0, σ y V γ G = 2V (γ Y ) γ I 2 as in(24). Case 2 (s T = LoS): Using the random variables U i,I and U i,Q as in Case 1 (s T = NLoS), we rewrite(20) as in (40). from Case 1 (s T = NLoS), the distribution of Y I and Y Q in (40), are given as in (41) and (42), respectively.C.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Fundamental Limits on Detection of UAVs by Existing Terrestrial RF Networks

Sinha¹,

Güvenç

Gursoy

2021

IEEE Open J. Commun. Soc.

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Detection of drones carries critical importance for safely and effectively managing unmanned aerial system traffic in the future. Given the ubiquitous presence of the drones across all kinds of environments in the near future, wide area drone detection and surveillance capability are highly desirable, which require careful planning and design of drone sensing networks. In this paper, we seek to meet this need by using the existing terrestrial radio frequency (RF) networks for passive sensing of drones. To this end we develop an analytical framework that provides the fundamental limits on the network-wide drone detection probability. In particular, we characterize the joint impact of the salient features of the terrestrial RF networks, such as the spatial randomness of the node locations, the directional 3D antenna patterns, and the mixed line of sight/non line of sight (LoS/NLoS) propagation characteristics of the air-to-ground (A2G) channels. Since the strength of the drone signal and the aggregate interference in a sensing network are fundamentally limited by the 3D network geometry and the inherent spatial randomness, we use tools from stochastic geometry to derive the closed-form expressions for the probabilities of detection, false alarm and coverage. This, in turn, demonstrates the impact of the sensor density, beam tilt angle, half power beam width (HPBW) and different degrees of LoS dominance, on the projected detection performance. Our analysis reveals optimal beam tilt angles, and sensor density that maximize the network-wide detection of the drones.

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Section: Drone Detection In Rf Sensing Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fundamental Limits on Detection of UAVs by Existing Terrestrial RF Networks

Sinha¹,

Güvenç

Gursoy

2021

IEEE Open J. Commun. Soc.

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show abstract

“…video provisioning [23], mobile edge computing [24], and aerial information sensing [25], can be a key solution for MBS services in cellular networks. In order to use the drones for cellular networks, theoretical performance analysis under consideration of practical antenna configurations [26] and mobility patterns [27] is essentially required.…”

Section: Introductionmentioning

confidence: 99%

Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles

et al. 2021

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In modern networks, the use of drones as mobile base stations (MBSs) has been discussed for coverage flexibility. However, the realization of drone-based networks raises several issues. One of critical issues is drones are extremely power-hungry. To overcome this, we need to characterize a new type of drones, so-called charging drones, which can deliver energy to MBS drones. Motivated by the fact that the charging drones also need to be charged, we deploy ground-mounted charging towers for delivering energy to the charging drones. We introduce a new energy-efficiency maximization problem, which is partitioned into two independently separable tasks. More specifically, as our first optimization task, two-stage charging matching is proposed due to the inherent nature of our network model, where the first matching aims to schedule between charging towers and charging drones while the second matching solves the scheduling between charging drones and MBS drones. We analyze how to convert the formulation containing non-convex terms to another one only with convex terms. As our second optimization task, each MBS drone conducts energy-aware time-average transmit power allocation minimization subject to stability via Lyapunov optimization. Our solutions enable the MBS drones to extend their lifetimes; in turn, network coverage-time can be extended.INDEX TERMS Cellular network, charging drone, coverage-time, mobile base station, scheduling.

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“…Receiving beamforming has been considered a promising technology for cellular communication to address the serves interference issue and support the emerging UAV applications [3] [4] [5] [6] [7]. Through adaptively adjusting the antenna radiation patterns, the array elements at BS could be combined constructively or destructively to form the peaks and nulls in the antenna beam, which provides the benefits of simultaneously enhancing the signal transmission and mitigating the adverse interferences [8]. The accurate Direction-of-arrival (DoA) estimation plays an important role in beamforming algorithms to achieve the optimal antenna response.…”

Section: Introductionmentioning

confidence: 99%

Location-Based Robust Beamforming Design for Cellular-Enabled UAV Communications

Miao

Luo

Min

et al. 2021

IEEE Internet Things J.

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Cellular communications have been regarded as promising approaches to deliver high-broadband communication links for Unmanned Aerial Vehicles (UAVs), which have been widely deployed to conduct various missions, e.g. precision agriculture, forest monitoring and border patrol. However, the unique features of aerial UAVs including high-altitude manipulation, three-dimension (3D) mobility, and rapid velocity changes, pose challenging issues to realize reliable cellular-enabled UAV communications, especially with the severe inter-cell interference generated by UAVs. To deal with this issue, we propose a novel position-based robust beamforming algorithm through complementarily integrating the navigation information and wireless channel information to improve the performance of cellularenabled UAV communications. Specifically, in order to achieve the optimal beam weight vector, the navigation information of the UAV system is innovatively exploited to predict the changes of Direction-of-arrival (DoA) angle. To fight against the high mobility of UAV operations, an optimization problem is formed by considering the tapered surface of DoA angle and solved to correct the inherent position error. Comprehensive simulation experiments are conducted and the results show that the proposed robust beamforming algorithm could achieve over 90% DoA estimation error reduction and up to 14dB SINR gain compared with five benchmark beamforming algorithms, including Linearly Constrained Minimum Variance (LCMV), Position-based beamforming, Diagonal Loading (DL), Robust Capon Beamforming (RCB) and Robust LCMV algorithm.

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Performance Analysis of Mobile Cellular-Connected Drones under Practical Antenna Configurations

Cited by 39 publications

References 18 publications

Fundamental Limits on Detection of UAVs by Existing Terrestrial RF Networks

Fundamental Limits on Detection of UAVs by Existing Terrestrial RF Networks

Joint Mobile Charging and Coverage-Time Extension for Unmanned Aerial Vehicles

Location-Based Robust Beamforming Design for Cellular-Enabled UAV Communications

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