Performance Analysis of Distributed Intelligent Reflective Surfaces for Wireless Communications

10/23/2020 ∙ by Diluka Loku Galappaththige, et al. ∙ 0

In this paper, a comprehensive performance analysis of a distributed intelligent reflective surfaces (IRS)-aided communication system is presented. First, the optimal signal-to-noise ratio (SNR), which is attainable through the direct and reflected channels, is quantified by controlling the phase-shifts of the distributed IRS. Next, this optimal SNR is statistically characterized by deriving tight approximations to the exact probability density function (PDF) and cumulative distribution function (CDF) for Nakagami-m fading.The accuracy/tightness of this statistical characterization is investigated by deriving the Kullback-Leibler divergence. Our PDF/CDF analysis is used to derive tight approximations/bounds for the outage probability, achievable rate, and average symbol error rate (SER) in closed-form. To obtain useful insights, the asymptotic outage probability and average SER are derived for the high SNR regime. Thereby, the achievable diversity order and array gains are quantified. Our asymptotic performance analysis reveals that the diversity order can be boosted by using distributed passive IRS without generating additional electromagnetic (EM) waves via active radio frequency chains. Our asymptotic rate analysis shows that the lower and upper rate bounds converge to an asymptotic limit in large reflective element regime in which the transmit power can be scaled inversely proportional to the square of the number of reflective elements.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.