Modeling UAV-aided Roadside Cell-Free Networks with Mat\'ern Hard-Core Point Processes

TL;DR

This paper models UAV-assisted roadside cell-free networks using stochastic geometry, introducing a Matérn Hard-Core process for UAV placement and analyzing coverage probability in a road-constrained environment.

Contribution

It presents a novel combined model of road-constrained APs and UAVs with minimum separation, providing analytical insights into coverage performance.

Findings

Coverage probability depends on UAV density and placement.

Distance-based power control improves network coverage.

UAV deployment parameters significantly impact system performance.

Abstract

This paper investigates a uncrewed aerial vehicles (UAV)-assisted cell-free architecture for vehicular networks in road-constrained environments. Roads are modeled using a Poisson Line Process (PLP), with multi-layer roadside access points (APs) deployed via 1-D Poisson Point Process (PPP). Each user forms a localized cell-free cluster by associating with the nearest AP in each layer along its corresponding road. This forms a road-constrained cell-free architecture. To enhance coverage, UAV act as an aerial tier, extending access from 1-D road-constrained layouts (embedded in 2-D) to 3-D. We employ a Mat\'ern Hard-Core (MHC) point process to model the spatial distribution of UAV base stations, ensuring a minimum safety distance between them. In order to enable tractable analysis of the aggregate signal from multiple APs, a distance-based power control scheme is introduced. Leveraging tools from stochastic geometry, we have studied the coverage probability. Furthermore, we analyze the impact of key system parameters on coverage performance, providing useful insights into the deployment and optimization of UAV-assisted cell-free vehicular networks.