Modeling Cellular-to-UAV Path-Loss for Suburban Environments

Al‐Hourani, Akram; Gomez, Karina

doi:10.1109/lwc.2017.2755643

Cited by 257 publications

(205 citation statements)

References 5 publications

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“…Therefore, their findings show that the UAV propagation channel requires altitude-dependent parameters for channel modeling. In contrast, [65] proposed an angle-dependent channel model to characterize propagation between the cellular base station and the UAV airborne platform. However, these studies have ignored connectivity disruption issues occurred in the UAV-cellular systems due to diffraction losses and nulls in the radiation pattern of the base station's antenna.…”

Section: Cellular-connected Uav Measurementsmentioning

confidence: 99%

“…The altitude-dependent path loss model was proposed in [64], where path loss and shadow fading decreased as the UAV altitude increased from 15 to 120 m and at about 100 m the propagation condition matched with that of free space. Conversely, in [65], the angle-dependent AG propagation channel model was PL(dB) = 10α log 10 (d), α =1.922 [54], α =0.93 [55], α =2.05 [56] [57]…”

Section: Empirical Channel Models From Measurement Campaignsmentioning

confidence: 99%

“…, where Ψ b s and φ b s are, respectively, the azimuth and the elevation angle observed from the base station, l b s, ma x and l b s, mi n are the distance between the base station and, respectively, the farthest and the nearest scatterer point, and V is the volume of the scattering region [20] The von Mises PDF of AOA as a function of azimuth angle: • Path loss model presented as closed-form expressions with correction factors based on environment conditions [47], UAV altitude [62], [64] and elevation angle [65] • Channel characterization depends upon the multipath resolution of the channel sounder, antenna design and propagation environment • Small-scale fading can be characterized by the superposition of all multipath components in the form of channel impulse response [45]- [48] • Size and payload constraints of different UAV types…”

Section: Vmentioning

confidence: 99%

“…To this end, channel characterization with USRP hardware, such as N-210 [46], B-210 [48], X-310 [102], [103] and B-200 mini can provide flexible platform due to lighter weight, low power consumption, wideband frequencies and capable to test different wireless communication protocols, such as multi-carrier and MIMO system in UAV communication framework. Other possible choices for channel measurement hardware used by different researchers are P410 UWB radio [47], autonomous mobile network scanner by Rohde & Schwarz [64] and 3G/4G enabled smart-phones [65], [74]. In addition, the effects of antenna placement on UAVs and the gain from UAV-MIMO system for both the AA and AG propagation are not well studied.…”

Section: A Uav Measurement Campaignsmentioning

confidence: 99%

See 3 more Smart Citations

A Survey of Channel Modeling for UAV Communications

Khuwaja

Chen

Zhao

et al. 2018

IEEE Commun. Surv. Tutorials

680

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: Cellular-connected Uav Measurementsmentioning

confidence: 99%

Section: Empirical Channel Models From Measurement Campaignsmentioning

confidence: 99%

Section: Vmentioning

confidence: 99%

Section: A Uav Measurement Campaignsmentioning

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

680

346

View full text Add to dashboard Cite

show abstract

“…The log-distance path loss model is widely accepted in a variety of practical applications. 15 There are also many examples of using the log-distance path loss model with a diffraction loss model. [16][17][18] As can be seen in Section 2, since the Deygout method overestimates the diffraction loss and the Bullington method underestimates it, the parameter ω 2 is used as shown in Equation (3) to obtain the proper diffraction loss value between the loss values calculated in the two methods.…”

Section: Proposed Methodsmentioning

confidence: 99%

A study on path loss prediction based on Tikhonov regularization using gradient adaptive step‐size in mountain areas

Lee

2020

Micro & Optical Tech Letters

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This paper presents a method of applying a regularization concept for more accurate path loss prediction in mountainous areas. Tikhonov regularization with gradient adaptive step-size is used to adjust the path loss model. Measurements were conducted in two mountainous areas to compare the path loss prediction performances. Prediction accuracies were improved by up to 3.42 and 0.95 dB in the average error and the SD of errors by the proposed method when compared to the nontuned log-distance path loss model and Edwards-Durkin model with diffraction models. K E Y W O R D S adaptive step-size, mountain areas, path loss prediction, regression, regularization

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3GPP TR 38.901 Channel Model

et al. 2021

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The 3rd Generation Partnership Project (3GPP) recommends an accurate and compatible stochastic channel model for 0–100 GHz frequency band, which is useful to design, optimize, and evaluate the fifth‐generation (5G) systems. This standardized model can be acquired by generating the large‐ and small‐scale fading models. This article firstly reviews the main existing channel models suitable for millimeter‐wave (mmWave) frequency band as well as the typical channel modeling methods, i.e. the deterministic and stochastic models. Secondly, the generation procedure of stochastic channel model in the 3GPP standard (TR 38.901) is illustrated in detail. The large‐ and small‐scale fading parts are also simulated to demonstrate the effect of channel parameters on the channel characteristics. Finally, as an alternative model in the 3GPP standard, the map‐based hybrid channel model is demonstrated and analyzed. When the digital map and material parameters of obstacles are known, this model can predict the channel characteristics accurately using the ray‐tracing (RT) method.

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Modeling Cellular-to-UAV Path-Loss for Suburban Environments

Cited by 257 publications

References 5 publications

A Survey of Channel Modeling for UAV Communications

A Survey of Channel Modeling for UAV Communications

A study on path loss prediction based on Tikhonov regularization using gradient adaptive step‐size in mountain areas

3GPP TR 38.901 Channel Model

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