Air–Ground Channel Characterization for Unmanned Aircraft Systems Part II: Hilly and Mountainous Settings

Sun, Ruoyu; Matolak, David W.

doi:10.1109/tvt.2016.2585504

Cited by 205 publications

(125 citation statements)

References 12 publications

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“…Some of the deterministic UAV channel models are reported in Table V. Over water [13], hilly [14] and suburban [15] Wideband AG Rician K ∼ 20-27 dB in C band and K ∼ 12 dB in L band [13], K=29.4 dB in C band and K=12.8 dB in L band [14], K=28.5 dB in C band and K=14 dB in L band [15] [ …”

Section: A Deterministicmentioning

confidence: 99%

“…The probability of the existence of the intermittent multipath component was estimated by the exponential distribution as a function of link distance. The TDL model with nine taps have been proposed for the mountainous terrain [14] and the suburban environment [15].…”

Section: B Stochastic Channel Modelmentioning

confidence: 99%

“…In [20], the geometric-based stochastic approach has been utilized for UAV channel modeling to analytically characterize a 2 × 2 MIMO enabled AG propagation in a 3D plane. In this case, the model was developed with the assump- TDL model [13] h(τ, t) = h 2−r a y (τ, t) + w 3 (t)A 3 (t) exp(−jϕ 3 (t))δ(τ − τ 3 (t)), h 2−r a y denotes FE2R or CE2R model, w 3 (t) ∈ {1, 0} represents presence/absence of the intermittent ray and modeled as p(d) = ae b d , A 3 is the amplitude of the intermittent ray and modeled by the Gaussian distribution, ϕ 3 ∈ {0, 2π } is the uniformly distributed phase of the intermittent ray, τ 3 is the excess delay of the intermittent ray and modeled as p(τ 3 ) = 1 µ e −(τ 3 −100/µ) , (a, b) = (0.17,-0.25) over sea water and (0.03,-0.15) over freshwater, µ= 17 ns, 6 ns ≤ τ 3 ≤ 7 ns, d: link distance [14], [15] …”

Section: Geometry-based Stochastic Channel Modelmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Channel Modeling for UAV Communications

Khuwaja

Chen

Zhao

et al. 2018

IEEE Commun. Surv. Tutorials

714

346

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Unmanned aerial vehicles (UAVs) have gained great interest for rapid deployment in both civil and military applications. UAV communication has its own distinctive channel characteristics compared with widely used cellular and satellite systems. Thus, accurate channel characterization is crucial for the performance optimization and design of efficient UAV communication systems. However, several challenges exist in UAV channel modeling. For example, propagation characteristics of UAV channels are still less explored for spatial and temporal variations in non-stationary channels. Also, airframe shadowing has not yet been investigated for small size rotary UAVs. This paper provides an extensive survey on the measurement campaigns launched for UAV channel modeling using low altitude platforms and discusses various channel characterization efforts. We also review the contemporary perspective of UAV channel modeling approaches and outline some future research challenges in this domain.

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Section: A Deterministicmentioning

confidence: 99%

Section: B Stochastic Channel Modelmentioning

confidence: 99%

Section: Geometry-based Stochastic Channel Modelmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Channel Modeling for UAV Communications

Khuwaja

Chen

Zhao

et al. 2018

IEEE Commun. Surv. Tutorials

714

346

View full text Add to dashboard Cite

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“…To assess the benefits of UAV deployment at mmWave frequencies, it is important to have a comprehensive and accurate channel model while taking into account mmWave signal propagation as well as the random effect of hovering fluctuations. For instance, even though channel modeling in the context of UAV communications has been discussed in recent studies [16]- [18], these prior works are restricted to sub-6 GHz bands whose results cannot directly extended to mmWave systems. Meanwhile, most of the prior art on mmWave communications [19]- [22] does not address the presence of UAVs, with the exception of a few recent works in [23]- [29].…”

Section: A Literature Reviewmentioning

confidence: 99%

Analytical Channel Models for Millimeter Wave UAV Networks Under Hovering Fluctuations

Dabiri

Safi

Parsaeefard

et al. 2020

IEEE Trans. Wireless Commun.

152

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The integration of unmanned aerial vehicles (UAVs) and millimeter wave (mmWave) wireless systems has been recently proposed to provide high data rate aerial links for next generation wireless networks. However, establishing UAVbased mmWave links is quite challenging due to the random fluctuations of hovering UAVs which can induce antenna gain mismatch between transmitter and receiver. To assess the benefit of UAV-based mmWave links, in this paper, tractable, closedform statistical channel models are derived for three UAV communication scenarios: (i) a direct UAV-to-UAV link, (ii) an aerial relay link in which source, relay, and destination are hovering UAVs, and (iii) a relay link in which a hovering UAV connects a ground source to a ground destination. The accuracy of the derived analytical expressions is corroborated by performing Monte-Carlo simulations. Numerical results are then used to study the effect of antenna directivity gain under different channel conditions for establishing reliable UAV-based mmWave links in terms of achieving minimum outage probability. It is shown that the performance of such links is largely dependent on the random fluctuations of hovering UAVs. Moreover, higher antenna directivity gains achieve better performance at low SNR regime. Nevertheless, at the high SNR regime, lower antenna directivity gains result in a more reliable communication link. The developed results can therefore be applied as a benchmark for finding the optimal antenna directivity gain of UAVs under the different levels of instability without resorting to time-consuming simulations.

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“…In this paper, we assume that the air-to-ground communication links between the UAV and the ground users are LoS-channels. In particular, the channel vector between the UAV and user k in time slot n is given by [30] h…”

Section: A Multiuser Uav Communication Systemmentioning

confidence: 99%

Multiuser MISO UAV Communications in Uncertain Environments With No-Fly Zones: Robust Trajectory and Resource Allocation Design

Sun

et al. 2020

IEEE Trans. Commun.

172

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In this paper, we investigate robust resource allocation algorithm design for multiuser downlink multiple-input single-output (MISO) unmanned aerial vehicle (UAV) communication systems, where we account for the various uncertainties that are unavoidable in such systems and, if left unattended, may severely degrade system performance.We jointly optimize the two-dimensional (2-D) trajectory and the transmit beamforming vector of the UAV for minimization of the total power consumption. The algorithm design is formulated as a non-convex optimization problem taking into account the imperfect knowledge of the angle of departure (AoD) caused by UAV jittering, user location uncertainty, wind speed uncertainty, and polygonal no-fly zones (NFZs). Despite the non-convexity of the optimization problem, we solve it optimally by employing monotonic optimization theory and semidefinite programming relaxation which yields the optimal 2-D trajectory and beamforming policy. Since the developed optimal resource allocation algorithm entails a high computational complexity, we also propose a suboptimal iterative low-complexity scheme based on successive convex approximation to strike a balance between optimality and computational complexity. Our simulation results reveal not only the significant power savings enabled by the proposed algorithms compared to two baseline schemes, but also confirm their robustness with respect to UAV jittering, wind speed uncertainty, and user location uncertainty. Moreover, our results unveil that the joint presence of wind speed uncertainty and NFZs has a considerable impact on the UAV trajectory. Nevertheless, by counteracting the wind speed uncertainty with the proposed robust design, we can simultaneously minimize the total UAV power consumption and ensure a secure trajectory that does not trespass any NFZ. ). This paper was presented in part at IEEE Globecom 2018 [1]. 2 be employed as aerial base stations to offer temporary communication links in a timely and cost-effective manner. Moreover, due to their high mobility and maneuverability, UAVs can adapt their trajectories based on the actual environment and terrain which improves system performance [3]. As a result, UAV-assisted communication systems have drawn significant attention from both academia and industry. For instance, the authors of [4] studied suboptimal UAV trajectory design for maximization of the energy-efficiency of UAV communication systems. The authors of [5] proposed a suboptimal joint trajectory, power allocation, and user scheduling algorithm for maximization of the minimum user throughput in multi-UAV systems.Secure UAV communications was investigated in [6] where the trajectory of a UAV and its transmit power were jointly optimized to maximize the system secrecy rate. The authors of [7] proposed solar-powered UAV communication systems and studied the jointly optimal resource allocation and UAV trajectory design for maximization of the system sum throughput. In fact, the throughput of UAV communication systems can be further i...

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Air–Ground Channel Characterization for Unmanned Aircraft Systems Part II: Hilly and Mountainous Settings

Cited by 205 publications

References 12 publications

A Survey of Channel Modeling for UAV Communications

A Survey of Channel Modeling for UAV Communications

Analytical Channel Models for Millimeter Wave UAV Networks Under Hovering Fluctuations

Multiuser MISO UAV Communications in Uncertain Environments With No-Fly Zones: Robust Trajectory and Resource Allocation Design

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