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Digital Predistortion for Hybrid MIMO Transmitters

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Digital Predistortion for Hybrid MIMO Transmitters. / Abdelaziz, Mahmoud; Anttila, Lauri; Brihuega, Alberto; Tufvesson, Fredrik; Valkama, Mikko.

In: IEEE Journal on Selected Topics in Signal Processing, Vol. 12, No. 3, 06.2018, p. 445-454.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Abdelaziz, M, Anttila, L, Brihuega, A, Tufvesson, F & Valkama, M 2018, 'Digital Predistortion for Hybrid MIMO Transmitters', IEEE Journal on Selected Topics in Signal Processing, vol. 12, no. 3, pp. 445-454. https://doi.org/10.1109/JSTSP.2018.2824981

APA

Abdelaziz, M., Anttila, L., Brihuega, A., Tufvesson, F., & Valkama, M. (2018). Digital Predistortion for Hybrid MIMO Transmitters. IEEE Journal on Selected Topics in Signal Processing, 12(3), 445-454. https://doi.org/10.1109/JSTSP.2018.2824981

Vancouver

Abdelaziz M, Anttila L, Brihuega A, Tufvesson F, Valkama M. Digital Predistortion for Hybrid MIMO Transmitters. IEEE Journal on Selected Topics in Signal Processing. 2018 Jun;12(3):445-454. https://doi.org/10.1109/JSTSP.2018.2824981

Author

Abdelaziz, Mahmoud ; Anttila, Lauri ; Brihuega, Alberto ; Tufvesson, Fredrik ; Valkama, Mikko. / Digital Predistortion for Hybrid MIMO Transmitters. In: IEEE Journal on Selected Topics in Signal Processing. 2018 ; Vol. 12, No. 3. pp. 445-454.

Bibtex - Download

@article{d8756180d13a436c8bd43b0bab3e79ad,
title = "Digital Predistortion for Hybrid MIMO Transmitters",
abstract = "This article investigates digital predistortion (DPD) linearization of hybrid beamforming large-scale antenna transmitters. We propose a novel DPD processing and learning technique for an antenna sub-array, which utilizes a combined signal of the individual power amplifier (PA) outputs in conjunction with a decorrelation-based learning rule. In effect, the proposed approach results in minimizing the nonlinear distortions in the direction of the intended receiver. This feature is highly desirable, since emissions in other directions are naturally weak due to beamforming. The proposed parameter learning technique requires only a single observation receiver, and therefore supports simple hardware implementation. It is also shown to clearly outperform the current state-of-the-art technique which utilizes only a single PA for learning. Analysis of the feedback network amplitude and phase imbalances reveals that the technique is robust even to high levels of such imbalances. Finally, we also show that the array system out-of-band emissions are well-behaving in all spatial directions, and essentially below those of the corresponding single-antenna transmitter, due to the combined effects of the DPD and beamforming.",
keywords = "5G, digital predistortion, hybrid beamforming, large-array transmitters, out-of-band emissions, power amplifiers",
author = "Mahmoud Abdelaziz and Lauri Anttila and Alberto Brihuega and Fredrik Tufvesson and Mikko Valkama",
year = "2018",
month = "6",
doi = "10.1109/JSTSP.2018.2824981",
language = "English",
volume = "12",
pages = "445--454",
journal = "IEEE Journal of Selected Topics in Signal Processing",
issn = "1932-4553",
publisher = "Institute of Electrical and Electronics Engineers",
number = "3",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Digital Predistortion for Hybrid MIMO Transmitters

AU - Abdelaziz, Mahmoud

AU - Anttila, Lauri

AU - Brihuega, Alberto

AU - Tufvesson, Fredrik

AU - Valkama, Mikko

PY - 2018/6

Y1 - 2018/6

N2 - This article investigates digital predistortion (DPD) linearization of hybrid beamforming large-scale antenna transmitters. We propose a novel DPD processing and learning technique for an antenna sub-array, which utilizes a combined signal of the individual power amplifier (PA) outputs in conjunction with a decorrelation-based learning rule. In effect, the proposed approach results in minimizing the nonlinear distortions in the direction of the intended receiver. This feature is highly desirable, since emissions in other directions are naturally weak due to beamforming. The proposed parameter learning technique requires only a single observation receiver, and therefore supports simple hardware implementation. It is also shown to clearly outperform the current state-of-the-art technique which utilizes only a single PA for learning. Analysis of the feedback network amplitude and phase imbalances reveals that the technique is robust even to high levels of such imbalances. Finally, we also show that the array system out-of-band emissions are well-behaving in all spatial directions, and essentially below those of the corresponding single-antenna transmitter, due to the combined effects of the DPD and beamforming.

AB - This article investigates digital predistortion (DPD) linearization of hybrid beamforming large-scale antenna transmitters. We propose a novel DPD processing and learning technique for an antenna sub-array, which utilizes a combined signal of the individual power amplifier (PA) outputs in conjunction with a decorrelation-based learning rule. In effect, the proposed approach results in minimizing the nonlinear distortions in the direction of the intended receiver. This feature is highly desirable, since emissions in other directions are naturally weak due to beamforming. The proposed parameter learning technique requires only a single observation receiver, and therefore supports simple hardware implementation. It is also shown to clearly outperform the current state-of-the-art technique which utilizes only a single PA for learning. Analysis of the feedback network amplitude and phase imbalances reveals that the technique is robust even to high levels of such imbalances. Finally, we also show that the array system out-of-band emissions are well-behaving in all spatial directions, and essentially below those of the corresponding single-antenna transmitter, due to the combined effects of the DPD and beamforming.

KW - 5G

KW - digital predistortion

KW - hybrid beamforming

KW - large-array transmitters

KW - out-of-band emissions

KW - power amplifiers

U2 - 10.1109/JSTSP.2018.2824981

DO - 10.1109/JSTSP.2018.2824981

M3 - Article

VL - 12

SP - 445

EP - 454

JO - IEEE Journal of Selected Topics in Signal Processing

JF - IEEE Journal of Selected Topics in Signal Processing

SN - 1932-4553

IS - 3

ER -