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SPARSE LINEAR PRECODERS FOR MITIGATING NONLINEARITIES IN MASSIVE MIMO

Abstract : Dealing with nonlinear effects of the radio-frequency (RF) chain is a key issue in the realization of very large-scale multi-antenna (MIMO) systems. Achieving the remarkable gains possible with massive MIMO requires that the signal processing algorithms systematically take into account these effects. Here, we present a computationally-efficient linear precoding method satisfying the requirements for low peak-to-average power ratio (PAPR) and low-resolution D/Aconverters (DACs). The method is based on a sparse regularization of the precoding matrix and offers advantages in terms of precoded signal PAPR as well as processing complexity. Through simulation, we find that the method substantially improves conventional linear precoders.
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https://hal.archives-ouvertes.fr/hal-03438247
Contributor : Inbar Fijalkow Connect in order to contact the contributor
Submitted on : Sunday, November 21, 2021 - 11:05:18 AM
Last modification on : Friday, January 7, 2022 - 3:44:25 AM
Long-term archiving on: : Tuesday, February 22, 2022 - 6:56:59 PM

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Amine Mezghani, Daniel Plabst, Lee A Swindlehurst, Inbar Fijalkow, Josef A Nossek. SPARSE LINEAR PRECODERS FOR MITIGATING NONLINEARITIES IN MASSIVE MIMO. 2021 IEEE Statistical Signal Processing Workshop (SSP 2021), Jul 2021, Rio, Brazil. ⟨hal-03438247⟩

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