Coverage Probability of Double-IRS Assisted Communication Systems

TL;DR

This paper derives a closed-form expression for the coverage probability in double-IRS assisted wireless systems, demonstrating improved performance and reduced computational complexity through large-scale statistics-based optimization.

Contribution

It provides a novel analytical expression for coverage probability in double-IRS systems and introduces an efficient RBM optimization method based on large-scale statistics.

Findings

Analytical coverage probability expression validated by numerical results.

Double-IRS systems outperform single-IRS in coverage performance.

RBM optimization based on statistical CSI reduces computational complexity.

Abstract

In this paper, we focus on the coverage probability of a double-intelligent reflecting surface (IRS) assisted wireless network and study the impact of multiplicative beamforming gain and correlated Rayleigh fading. In particular, we obtain a novel closed-form expression of the coverage probability of a single-input single-output (SISO) system assisted by two large IRSs while being dependent on the corresponding arbitrary reflecting beamforming matrices (RBMs) and large-scale statistics in terms of correlation matrices. Taking advantage of the large-scale statistics, i.e., statistical channel state information (CSI), we perform optimization of the RBMs of both IRSs once per several coherence intervals rather than at each interval. Hence, we achieve a reduction of the computational complexity, otherwise increased in multi-IRS-assisted networks during their RBM optimization. Numerical results validate the analytical expressions even for small IRSs, confirm enhanced performance over the conventional single-IRS counterpart, and reveal insightful properties.