Tractable Optical Channel Modeling Between UAVs

Dabiri, Mohammad Taghi; Sadough, Seyed Mohammad Sajad; Ansari, Imran Shafique

doi:10.1109/tvt.2019.2940226

Cited by 74 publications

(62 citation statements)

References 17 publications

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“…, and c 5 = − cot µ φ cos µ θ are constants. Proof: The proof is given in Appendix D. Note that the PDF of f hg (h) in (20) has an indeterminate form at h = 0. Its value can be found for q = 1 as…”

Section: B Statistical Gml Model For Independent Gaussian Fluctuationsmentioning

confidence: 99%

“…. Proof: The proof follows by substitution of q = 1 and Ω = 2(σ 2 p + µ 2 x σ 2 o ) into (20). Depending on the value of , the PDF of the GML in (24) shows the following behavior.…”

Section: B Statistical Gml Model For Independent Gaussian Fluctuationsmentioning

confidence: 99%

“…Since g y and g z are independent zero-mean Gaussian RVs with non-identical variances, u follows a Hoyt (Nakagami-q) distribution with PDF f u (u) = 1+q 2 qΩ u exp − (1+q 2 ) 2 4q 2 Ω u 2 I 0 1−q 4 4q 2 Ω u 2 , where q = min{λ1,λ2} max{λ1,λ2} and Ω = λ 1 + λ 2 [13], [36]. Substituting f u (u) into (19), the PDF of the GML can be obtained as in (20). This completes the proof.…”

Section: Appendix Dmentioning

confidence: 99%

“…Moreover, in recent work, the authors of [20]- [22] developed statistical channel models for FSO links connecting different UAVs assuming orthogonal beams. In particular, the authors of [20] investigated the outage probability, which was defined as the probability that the transmitting UAV falls out of the receiver's field-of-view (FoV) due to fluctuations of the orientation of the receiving UAV. Moreover, in [21], [22], adaptive beam control techniques were proposed to cope with the fluctuations of the positions and orientations of the transmitting and receiving UAVs.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Modeling of the FSO Fronthaul Channel for UAV-Based Communications

Najafi

Ajam

Jamali

et al. 2020

IEEE Trans. Commun.

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In this paper, we investigate the statistics of the free space optics (FSO) communication channel between a hovering unmanned aerial vehicle (UAV) and a central unit. Two unique characteristics make UAV-based FSO systems significantly different from conventional FSO systems with static transceivers. First, for UAV-based FSO systems, the incident laser beam is not always orthogonal to the receiver lens plane. Second, both position and orientation of the UAV fluctuate over time due to dynamic wind load, inherent random air fluctuations in the atmosphere around the UAV, and internal vibrations of the UAV. On the contrary, for conventional FSO systems, the laser beam is always perpendicular to the receiver lens plane and the relative movement of the transceivers is limited. In this paper, we develop a novel channel model for UAV-based FSO systems by quantifying the corresponding geometric and misalignment losses (GML), while taking into account the non-orthogonality of the laser beam and the random fluctuations of the position and orientation of the UAV. In particular, for diverse weather conditions, we propose different fluctuation models for the position and orientation of the UAV and derive corresponding statistical models for the GML. We further analyze the performance of a UAV-based FSO link in terms of outage probability and ergodic rate and simplify the resulting analytical expressions for the high signal-to-noise ratio (SNR) regime. Finally, simulations validate the accuracy of the presented analysis and provide important insights for system design. For instance, we show that for a given variance of the fluctuations, the beam width should be properly adjusted to minimize the outage probability. introduce the following new challenges: i) For conventional FSO links, it is typically assumed that the laser beam is orthogonal with respect to (w.r.t.) the receiver lens plane, as orthogonality maximizes the amount of laser power collected by the photo-detector (PD) located behind the lens [12]. However, orthogonality may not hold for UAV-based FSO communication systems. For example, the position of a UAV may depend on the locations and traffic needs of the users, while the CU may not be able to adjust the orientation of the receiver lens due to limited mechanical capabilities. In addition, using one receiver lens and multiple PDs [15], [16], the CU may receive data from several UAVs having different positions. Hence, it is not possible to orthogonally align the laser beams of all UAVs with the receiver lens plane. ii) Unlike 1 The receiver can only capture the fraction of power that falls on its lens. This phenomenon is known as geometric loss. Moreover, misalignment of the center of the optical beam and the center of the receiver lens further increases the geometric loss. This phenomenon is known as misalignment loss [12].

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Section: B Statistical Gml Model For Independent Gaussian Fluctuationsmentioning

confidence: 99%

“…. Proof: The proof follows by substitution of q = 1 and Ω = 2(σ 2 p + µ 2 x σ 2 o ) into (20). Depending on the value of , the PDF of the GML in (24) shows the following behavior.…”

Section: B Statistical Gml Model For Independent Gaussian Fluctuationsmentioning

confidence: 99%

Section: Appendix Dmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical Modeling of the FSO Fronthaul Channel for UAV-Based Communications

Najafi

Ajam

Jamali

et al. 2020

IEEE Trans. Commun.

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show abstract

“…Moreover, K, Malaga (M) and Exponentiated Weibull (EW) fading can be used in AtA communication. Specifically, K distribution can precisely model strong turbulence induced fading, whereas M is a general distribution model that can cover classical turbulence channels, including Log-normal, Gamma-Gamma, and K [14], [15]. Compared to its counterparts, EW can be adopted for the AtA communication as it can model weakto-strong turbulence induced fading for various aperture sizes, including point-like receive apertures (3 mm), when aperture averaging takes place [16].…”

Section: Background a Types Of Arsmentioning

confidence: 99%