Demystifying the Power Scaling Law of Intelligent Reflecting Surfaces and Metasurfaces
August 08, 2019 Β· Declared Dead Β· π IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
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Authors
Emil BjΓΆrnson, Luca Sanguinetti
arXiv ID
1908.03133
Category
eess.SP: Signal Processing
Cross-listed
cs.IT
Citations
65
Venue
IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Last Checked
5 months ago
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.
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