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Power Normalization in Massive MIMO Systems: How to Scale Down the Number of Antennas

Abstract : This work considers the downlink of a massive MIMO system in which L base stations (BSs) of N antennas each communicate with K single-antenna user equipments randomly positioned in the coverage area. Within this setting, we are interested in studying the effect of power normalization on the sum rate of the system when maximum ratio transmission (MRT) or zero forcing (ZF) are employed as precoding schemes. In particular, we consider the most common two power normal-ization methods known as vector and matrix normalizations. The analysis is conducted assuming that N and K grow large with a non-trivial ratio K/N under the assumption that the data transmission in each cell is affected by channel estimation errors, pilot contamination, an arbitrary large scale attenuation, and antenna correlation at the BSs. The asymptotic results are instrumental to get insights and make comparisons. For medium to high signal-to-noise ratios, simulations and theory concur to show that vector normalization largely outperforms matrix normalization for both MRT and ZF and thus allows to scale down the number of antennas required to achieve a target sum rate.
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Contributor : Romain Couillet <>
Submitted on : Tuesday, May 19, 2020 - 4:47:19 PM
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  • HAL Id : hal-01633454, version 1


Meysam Sadeghi, Luca Sanguinetti, Romain Couillet, Chau Yuen. Power Normalization in Massive MIMO Systems: How to Scale Down the Number of Antennas. IEEE International Conference on Communications, 2016, Kuala Lumpur, Malaysia. ⟨hal-01633454⟩



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