Stochastic Geometry Based Performance Analysis of IRS- Aided MMWave Networks

Abstract

Wireless communication society aims for higher frequency bands for the unused bandwidths. However higher frequency comes with its drawbacks such as the high penetration loss. The concept of intelligent reflecting surfaces (IRSs) has recently become a popular topic for various use cases including coverage enhancement. Through reflecting signals in the desired and programmed direction, coverage holes may be eliminated. In previous works, coverage analysis of IRS-aided networks was done for several different channel and system model assumptions. However, most of the works do not address the blockage effect, and those that address use an unpractical path loss (PL) model for the indirect link which is the sum-distance PL model. It is envisioned that the primary benefit of IRS would be to aid mmWave communication in the presence of blockages. In this thesis, we consider a stochastic geometry (SG)-based network to model the locations of two-dimensional blockages (typically buildings), base stations (BSs), and IRSs. We also consider the the product-distance PL model, which is a more suitable PL model for the far-field as opposed to the commonly used sum-distance model. First, we propose a gamma approximation for the nearest distance distributions and derive a closed-form expression for the distribution of the product-distance of the indirect link. We investigate the coverage probability for the line-of-sight (LoS) dependent network. In the numerical results, we compare our proposed analytical approximations with simulations. We also investigate the coverage gains of IRS-aided systems. We observed that up to a 45\% boost in the coverage probability can be obtained with the use of the IRS.