Downlink Coverage Analysis for a Finite 3D Wireless Network of Unmanned Aerial Vehicles
Vishnu Vardhan Chetlur, Harpreet S. Dhillon

TL;DR
This paper analyzes the downlink coverage probability in a finite 3D UAV network modeled as a binomial point process, deriving exact and approximate expressions considering Nakagami-m fading and LOS scenarios.
Contribution
It provides a novel exact expression for coverage probability in finite UAV networks and introduces an approximation method for LOS-dominant conditions.
Findings
Coverage probability varies with UAV height and receiver location.
Derived bounds for coverage probability using Berry-Esseen theorem.
Approximate coverage probability effectively captures dominant interferer effects.
Abstract
In this paper, we consider a finite network of unmanned aerial vehicles (UAVs) serving a given region. Modeling this network as a uniform binomial point process (BPP), we derive the downlink coverage probability of a reference receiver located at an arbitrary position on the ground assuming Nakagami- fading for all wireless links. The reference receiver is assumed to connect to its closest transmitting node as is usually the case in cellular systems. After deriving the distribution of distances from the reference receiver to the serving and interfering nodes, we derive an exact expression for downlink coverage probability in terms of the derivative of Laplace transform of interference power distribution. In the downlink of this system, it is not unusual to encounter scenarios in which the line-of-sight (LOS) component is significantly stronger than the reflected multipath components.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsUAV Applications and Optimization · Advanced MIMO Systems Optimization · Millimeter-Wave Propagation and Modeling
