# Demystifying the Power Scaling Law of Intelligent Reflecting Surfaces and Metasurfaces

## TL;DR

This paper critically examines the claimed quadratic SNR scaling of IRSs, demonstrating analytically and numerically that massive MIMO consistently outperforms IRSs in SNR, challenging previous assumptions about IRS advantages.

## Contribution

The paper provides a rigorous analytical proof and numerical analysis showing that massive MIMO always yields higher SNR than IRSs, contradicting prior claims of IRS's superior scaling.

## Key findings

- mMIMO always provides higher SNR than IRSs
- IRS requires a very large number of elements to match mMIMO performance
- Previous interpretations of IRS SNR scaling are incorrect

## Abstract

Intelligent reflecting surfaces (IRSs) have recently attracted the attention of communication theorists as a means to control the wireless propagation channel. It has been shown that the signal-to-noise ratio (SNR) of a single-user IRS-aided transmission increases as $N^2$, with $N$ being the number of passive reflecting elements in the IRS. This has been interpreted as a major potential advantage of using IRSs, instead of conventional Massive MIMO (mMIMO) whose SNR scales only linearly in $N$. This paper shows that this interpretation is incorrect. We first prove analytically that mMIMO always provides higher SNRs, and then show numerically that the gap is substantial; a very large number of reflecting elements is needed for an IRS to obtain SNRs comparable to mMIMO.