Sparse Linear Precoders for Mitigating Nonlinearities in Massive MIMO

Amine Mezghani; Daniel Plabst; Lee A. Swindlehurst; Inbar Fijalkow; Josef A. Nossek

doi:10.1109/SSP49050.2021.9513791

Sparse Linear Precoders for Mitigating Nonlinearities in Massive MIMO

Amine Mezghani, Daniel Plabst, Lee A. Swindlehurst, Inbar Fijalkow, Josef A. Nossek

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	2021 IEEE Statistical Signal Processing Workshop, SSP 2021
Publisher	IEEE Computer Society
Pages	391-395
Number of pages	5
ISBN (Electronic)	9781728157672
DOIs	https://doi.org/10.1109/SSP49050.2021.9513791
State	Published - 11 Jul 2021
Externally published	Yes
Event	21st IEEE Statistical Signal Processing Workshop, SSP 2021 - Virtual, Rio de Janeiro, Brazil Duration: 11 Jul 2021 → 14 Jul 2021

Publication series

Name	IEEE Workshop on Statistical Signal Processing Proceedings
Volume	2021-July

Conference

Conference	21st IEEE Statistical Signal Processing Workshop, SSP 2021
Country/Territory	Brazil
City	Virtual, Rio de Janeiro
Period	11/07/21 → 14/07/21

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10.1109/SSP49050.2021.9513791

Cite this

Mezghani, A., Plabst, D., Swindlehurst, L. A., Fijalkow, I., & Nossek, J. A. (2021). Sparse Linear Precoders for Mitigating Nonlinearities in Massive MIMO. In 2021 IEEE Statistical Signal Processing Workshop, SSP 2021 (pp. 391-395). Article 9513791 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2021-July). IEEE Computer Society. https://doi.org/10.1109/SSP49050.2021.9513791

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title = "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.",

author = "Amine Mezghani and Daniel Plabst and Swindlehurst, {Lee A.} and Inbar Fijalkow and Nossek, {Josef A.}",

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year = "2021",

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doi = "10.1109/SSP49050.2021.9513791",

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Mezghani, A, Plabst, D, Swindlehurst, LA, Fijalkow, I & Nossek, JA 2021, Sparse Linear Precoders for Mitigating Nonlinearities in Massive MIMO. in 2021 IEEE Statistical Signal Processing Workshop, SSP 2021., 9513791, IEEE Workshop on Statistical Signal Processing Proceedings, vol. 2021-July, IEEE Computer Society, pp. 391-395, 21st IEEE Statistical Signal Processing Workshop, SSP 2021, Virtual, Rio de Janeiro, Brazil, 11/07/21. https://doi.org/10.1109/SSP49050.2021.9513791

Sparse Linear Precoders for Mitigating Nonlinearities in Massive MIMO. / Mezghani, Amine; Plabst, Daniel; Swindlehurst, Lee A. et al.
2021 IEEE Statistical Signal Processing Workshop, SSP 2021. IEEE Computer Society, 2021. p. 391-395 9513791 (IEEE Workshop on Statistical Signal Processing Proceedings; Vol. 2021-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Sparse Linear Precoders for Mitigating Nonlinearities in Massive MIMO

AU - Mezghani, Amine

AU - Plabst, Daniel

AU - Swindlehurst, Lee A.

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AU - Nossek, Josef A.

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AB - 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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M3 - Conference contribution

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ER -

Sparse Linear Precoders for Mitigating Nonlinearities in Massive MIMO

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