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Cited by 423

The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.

Power Scaling Laws and Near-Field Behaviors of Massive MIMO and Intelligent Reflecting Surfaces

Open access
Bjornson, Emil, Sanguinetti, Luca·2020·IEEE Open Journal of the Communications Society

Abstract

The use of large arrays might be the solution to the capacity problems in wireless communications. The signal-to-noise ratio (SNR) grows linearly with the number of array elements N when using Massive MIMO receivers and half-duplex relays. Moreover, intelligent reflecting surfaces (IRSs) have recently attracted attention since these can relay signals to achieve an SNR that grows as N2, which seems like a major benefit. In this article, we use a deterministic propagation model for a planar array of arbitrary size, to demonstrate that the mentioned SNR behaviors, and associated power scaling laws, only apply in the far-field. They cannot be used to study the regime where N → ∞. We derive an exact channel gain expression that captures three essential near-field behaviors and use it to revisit the power scaling laws. We derive new finite asymptotic SNR limits but also conclude that these are unlikely to be approached in practice. We further prove that an IRS-aided setup cannot achieve a higher SNR than an equal-sized Massive MIMO setup, despite its faster SNR growth. We quantify analytically how much larger the IRS must be to achieve the same SNR. Finally, we show that an optimized IRS does not behave as an “anomalous” mirror but can vastly outperform that benchmark.

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Metadata

Authors
Bjornson, E., & Sanguinetti, L.
Venue
IEEE Open Journal of the Communications Society
Published
2020
Volume
1
Pages
1306-1324
ISSN
2644-125X (ISSN-L)
OpenAlex
W3006557588
Cited by
423
References
49
FWCI
21.27
Type
journal article
Language
en
Open access
gold

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Bjornson, E., & Sanguinetti, L. (2020). Power Scaling Laws and Near-Field Behaviors of Massive MIMO and Intelligent Reflecting Surfaces. IEEE Open Journal of the Communications Society. https://doi.org/10.1109/ojcoms.2020.3020925

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.