Demystifying the Power Scaling Law of Intelligent Reflecting Surfaces and Metasurfaces

August 08, 2019 Β· Declared Dead Β· πŸ› 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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