System-Level Performance Analysis in 3D Drone Mobile Networks

Huang, Jifu; Shojaeifard, Arman; Tang, Jie; Wong, Kai-Kit

doi:10.1007/978-3-030-63941-9_23

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…According to the independence of homogeneous PPP Φ, the drones are divided into two parts (i.e., LOS drones and NLOS drones). The average number of LOS and NLOS drones areK LOS = 4 3 πD 3 λ andK NLOS = 4 3 π R 3 − D 3 λ, respectively [45].…”

Section: B Propagation Modelmentioning

confidence: 99%

Drone Mobile Networks: Performance Analysis Under 3D Tractable Mobility Models

et al. 2021

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Reliable wireless communication networks are a significant but challenging mission for postdisaster areas and hotspots in the era of information. However, with the maturity of unmanned aerial vehicle (UAV) technology, drone mobile networks have attracted considerable attention as a prominent solution for facilitating critical communications. This paper provides a system-level analysis for drone mobile networks on a finite three-dimensional (3D) space. Our aim is to explore the fundamental performance limits of drone mobile networks taking into account practical considerations. Most existing works on mobile drone networks use simplified mobility models (e.g., fixed height), but the movement of the drones in practice is significantly more complicated, which leads to difficulties in analyzing the performance of the drone mobile networks. Hence, to tackle this problem, we propose a stochastic geometry-based framework with a number of different mobility models including a random Brownian motion approach. The proposed framework allows to circumvent the extremely complex reality model and obtain upper and lower performance bounds for drone networks in practice. Also, we explicitly consider certain constraints, such as the small-scale fading characteristics relying on line-of-sight (LOS) and non line-of-sight (NLOS) propagation, and multiantenna operations. The validity of the mathematical findings is verified via Monte-Carlo (MC) simulations for various network settings. In addition, the results reveal some design guidelines and important trends for the practical deployment of drone networks.

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Section: B Propagation Modelmentioning

confidence: 99%