Coverage and Rate Analysis for Unmanned Aerial Vehicle Base Stations with LoS/NLoS Propagation

Amroune, Mohamed; Yanıkömeroğlu, Halim

doi:10.1109/glocomw.2018.8644511

Cited by 59 publications

(44 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the similar lines, the authors of [13] investigated the coexistence of BSs and DBSs using probabilistic line-of-sight (LoS) and non-line-of-sight (NLoS) propagation models [18], where the locations of BSs and DBSs are modeled as a superposition of a Poisson point process (PPP) and a BPP. The work presented in [14] considered a network of DBSs modeled as a PPP serving ground UEs. In particular, incorporating LoS and NLoS propagation models, the authors derived approximations for the coverage probability and the received rate in the network.…”

Section: A Related Workmentioning

confidence: 99%

“…We now state the main result in the next proposition. dz ds, (14) when l ≤ vt, respectively. Otherwise, we have F L (l; t) = 1 and f L (l; t) = 0.…”

Section: Rw and Rwp Mobility Modelsmentioning

confidence: 99%

“…Note that when n = 1, we have L(t) = vt and the cdf becomes P[vt ≤ l, vt < R 1 ] = (1 − F R (vt))1(l − vt). Differentiating the derived cdf with respect to l using the Leibniz integral rule for two-dimensional integrals, we end up with the pdf of L(t) as written in (14).…”

Section: F Proof Of Propositionmentioning

confidence: 99%

See 2 more Smart Citations

Performance Characterization of Canonical Mobility Models in Drone Cellular Networks

Banagar

Dhillon

2020

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

In this paper, we characterize the performance of several canonical mobility models in a drone cellular network in which drone base stations (DBSs) serve user equipments (UEs) on the ground.In particular, we consider the following four mobility models: (i) straight line (SL), (ii) random stop (RS), (iii) random walk (RW), and (iv) random waypoint (RWP), among which the SL mobility model is inspired by the simulation models used by the third generation partnership project (3GPP) for the placement and trajectory of drones, while the other three are well-known canonical models (or their variants) that offer a useful balance between realism and tractability. Assuming the nearest-neighbor association policy, we consider two service models for the UEs: (i) UE independent model (UIM), and (ii) UE dependent model (UDM). While the serving DBS follows the same mobility model as the other DBSs in the UIM, it is assumed to fly towards the UE of interest in the UDM and hover above its location after reaching there. The main contribution of this paper is a unified approach to characterize the point process of DBSs for all the mobility and service models. Using this, we provide exact mathematical expressions for the average received rate and the session rate as seen by the typical UE. Further, using tools from calculus of variations, we concretely demonstrate that the simple SL mobility model provides a lower bound on the performance of other general mobility models (including the ones in which drones follow curved trajectories) as long as the movement of each drone in these models is independent and identically distributed (i.i.d.). To the best of our knowledge, this is the first work that provides a rigorous analysis of key canonical mobility models for an infinite drone cellular network and establishes useful connections between them.

show abstract

Section: A Related Workmentioning

confidence: 99%

“…We now state the main result in the next proposition. dz ds, (14) when l ≤ vt, respectively. Otherwise, we have F L (l; t) = 1 and f L (l; t) = 0.…”

Section: Rw and Rwp Mobility Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Performance Characterization of Canonical Mobility Models in Drone Cellular Networks

Banagar

Dhillon

2020

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

show abstract

“…Outage probability with the environment of the main link em and that of the interference link e I probability. However, the works in [23]- [27] used the path loss exponents and channel fading parameters, which are constant, not changed by the horizontal distance and the vertical distance of the communication link. The work in [28] used the different path loss exponents according to the UAV height, while the channel fading parameters are constant.…”

Section: (D)mentioning

confidence: 99%

“…Specifically, only the path loss is used for channels without fading in [20], [21], [29]- [31], [33], or the fact that the LoS probability can be different according to the locations of the UAV was not considered in [22], [32]. In addition, the works in [23]- [28] considered the different channel fadings depending on the LoS…”

Section: Introductionmentioning

confidence: 99%

Impact of an Interfering Node on Unmanned Aerial Vehicle Communications

Kim

Lee

2019

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Unlike terrestrial communications, unmanned aerial vehicle (UAV) communications have some advantages such as the line-of-sight (LoS) environment and flexible mobility. However, the interference will be still inevitable. In this paper, we analyze the effect of an interfering node on the UAV communications by considering the LoS probability and different channel fading for LoS and non-line-of-sight (NLoS) links, which are affected by horizontal and vertical distances of the communication link. We then derive a closed-form outage probability in the presence of an interfering node for all the possible scenarios and environments of main and interference links. After discussing the impacts of transmitting and interfering node parameters on the outage probability, we show the existence of the optimal height of the UAV that minimizes the outage probability. We also show the NLoS environment can be better than the LoS environment if the average received power of the interference is more dominant than that of the transmitting signal on UAV communications. Finally, we analyze the network outage probability for the case of multiple interfering nodes using stochastic geometry and the outage probability of the single interfering node case, and show the effect of the interfering node density on the optimal height of the UAV. Index Terms-Unmanned aerial vehicle, interfering node, airto-air channel, line-of-sight probability, outage probability P m , β I (ℓ I ) = ℓ −αI(ℓI) I P I .(2) 1 Note that the result of this paper can be readily extended for the multiple interfering nodes case as presented in Section IV. However, the analysis results will be complicated and give fewer insights. In addition, the communication performance is generally determined by one critical interfering node, especially in low outage probability region [36]. Therefore, we focus on the one interfering node case in this work, but the performance for the multiple interfering nodes case is also presented in simulation results of Section V.

show abstract

System-Level Performance Analysis in 3D Drone Mobile Networks

Huang

Shojaeifard

Tang

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

We present a system-level anaysis for drone mobile networks on a finite three-dimensional (3D) space. A performance boundary derived by deterministic random (Brownian) motion model over Nakagamim fading interfering channels is developed. This method allows us to circumvent the extremely complex reality model and obtain the upper and lower performance bounds of actual drone mobile networks. The validity and advantages of the proposed framework are confirmed via extensive Monte-Carlo(MC) simulations. The results reveal several important trends and design guidelines for the practical deployment of drone mobile networks.

show abstract

Coverage and Rate Analysis for Unmanned Aerial Vehicle Base Stations with LoS/NLoS Propagation

Cited by 59 publications

References 18 publications

Performance Characterization of Canonical Mobility Models in Drone Cellular Networks

Performance Characterization of Canonical Mobility Models in Drone Cellular Networks

Impact of an Interfering Node on Unmanned Aerial Vehicle Communications

System-Level Performance Analysis in 3D Drone Mobile Networks

Contact Info

Product

Resources

About