Connecting flying backhauls of UAVs to enhance vehicular networks with fixed 5G NR infrastructure

TL;DR

This paper analyzes how UAV backhauls can extend vehicular network connectivity in urban environments using stochastic and fractal geometry models, providing bounds, placement strategies, and simulation validation.

Contribution

It introduces an analytical framework for UAV backhaul deployment in urban vehicular networks, including bounds, placement strategies, and simulation validation.

Findings

Coverage increases with the number of gNBs and UAVs.

Optimal placement of drone garages reduces flight-to-coverage time.

Analytical bounds match simulation results.

Abstract

This paper investigates moving networks of Unmanned Aerial Vehicles (UAVs), such as drones, as one of the innovative opportunities brought by the 5G. With a main purpose to extend connectivity and guarantee data rates, the drones require hovering locations due to limitations such as flight time and coverage surface. We provide analytic bounds on the requirements in terms of connectivity extension for vehicular networks served by fixed Enhanced Mobile BroadBand (eMBB) infrastructure, where both vehicular networks and infrastructures are modeled using stochastic and fractal geometry as a model for urban environment. We prove that assuming $n$ mobile nodes (distributed according to a hyperfractal distribution of dimension $d_F$) and an average of $\rho$ Next Generation NodeB (gNBs), distributed like an hyperfractal of dimension $d_r$ if $\rho=n^\theta$ with $\theta>d_r/4$ and letting $n$ tending to infinity (to reflect megalopolis cities), then the average fraction of mobile nodes not covered by a gNB tends to zero like $O\left(n^{-\frac{(d_F-2)}{d_r}(2\theta-\frac{d_r}{2})}\right)$. Interestingly, we then prove that the average number of drones, needed to connect each mobile node not covered by gNBs is comparable to the number of isolated mobile nodes. We complete the characterisation by proving that when $\theta<d_r/4$ the proportion of covered mobile nodes tends to zero. We provide insights on the intelligent placement of the "garage of drones", the home location of these nomadic infrastructure nodes, such as to minimize what we call the "flight-to-coverage time". We provide a fast procedure to select the relays that will be garages (and store drones) in order to minimize the number of garages and minimize the delay. Finally we confirm our analytical results using simulations carried out in Matlab.