Line Process Modelling and Performance Analysis of Intelligent Reflecting Surface-Assisted Vehicular Networks

Vehicular networks require substantial communication links between infrastructure and vehicles to support high data rate, while the obstacles in the road will deteriorate channel capacity. Considering the attributes of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathcal {O}}({N^{2}})$</tex-math></inline-formula> reflecting beamforming gain and reconstructing line-of-sight (LOS) links, intelligent reflecting surface (IRS) could significantly improve the vehicle-to-infrastructure (V2I) channel capacity, while system-level analysis via stochastic geometry has not been analyzed. This paper proposes line process model for IRS-assisted vehicular networks and analyzes system performance via stochastic geometry, where base stations (BSs), IRSs and obstacles are distributed according to one-dimensional (1D) Poisson point process (PPP) and IRS will provide virtual LOS link when V2I link is blocked by obstacles. Specifically, the difference between IRS perfect reflecting gain and random scattering is taken into account, where the signal from the serving BS is reflecting beamformed by IRS while the interference from the neighboring BSs is randomly scattered. In addition, the blockage effect is modeled by Bernoulli variable and the trade-off between IRS deployment and obstacle density can be exploited. Theoretical results show that, system coverage probability is significantly improved by 81<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> with the optimal deployment density of IRS compared with no IRS scenario.