Decorrelation-based Piecewise Digital Predistortion: Operating Principle and RF Measurements

Abdelaziz, Mahmoud; Anttila, Lauri; Brihuega, Alberto; Allén, Markus; Valkama, Mikko

doi:10.1109/iswcs.2019.8877236

Cited by 6 publications

(13 citation statements)

References 16 publications

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“…• We formulate a piecewise (PW) CL DPD structure along with an efficient parameter learning entity that are together able to successfully linearize a PA or an array of PAs under very nonlinear conditions. The proposed solution is shown to outperform the early work in [26] and the widely adopted ILA-based single-polynomial DPD in [11], [18]- [22] by a wide margin. • For proper PW modelling, we propose a novel region partitioning algorithm, specifically tailored for PA modeling and linearization.…”

Section: B Novelty and Contributionsmentioning

confidence: 87%

“…In this article, building partially on our initial early work in [26], we describe efficient DPD processing and learning solutions for linearizing strongly nonlinear mmWave active arrays. The main contributions of the article can be described and summarized as follows:…”

Section: B Novelty and Contributionsmentioning

confidence: 99%

“…However, since in the proposed PW-DPD system the learning for all the regions is done jointly, the learning algorithm inherently ensures continuity when minimizing the nonlinear distortion, provided that it fully converges to the desired solution, as discontinuities are generally one source of nonlinear distortion. This will be illustrated with measurement examples in Section IV, and is one clear difference compared to the solution described in [26].…”

Section: B Gradient-based Pw-decorrelation Dpd Learningmentioning

confidence: 99%

See 2 more Smart Citations

Piecewise Digital Predistortion fo mmWave Active Antenna Arrays: Algorithms and Measurements

Brihuega,

Abdelaziz,

Anttila

et al. 2020

Preprint

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In this paper, we describe a novel framework for digital predistortion (DPD) based linearization of strongly nonlinear millimeter-wave active antenna arrays. Specifically, we formulate a piecewise (PW) closed-loop (CL) DPD solution and low-complexity gradient-adaptive parameter learning algorithms, together with a region partitioning method, that can efficiently handle deep compression of the PA units. The impact of beamsteering on the DPD performance is studied, showing strong beam-dependence, thus necessitating frequent updating of the DPD. In order to facilitate fast adaptation, an inexpensive, non-iterative, pruning algorithm is introduced, which allows to significantly reduce the amount of model coefficients. The proposed methods are validated with extensive over-the-air RF measurements on a 64-element active antenna array transmitter operating at 28 GHz carrier frequency and transmitting a 400 MHz 5G New Radio (NR) standard-compliant orthogonal frequency division multiplexing waveform. The obtained results demonstrate the excellent linearization capabilities of the proposed solution, conforming to the new 5G NR requirements for frequency range 2 (FR2) in terms of both inband waveform quality and out-of-band emissions. The proposed PW-CL DPD is shown to outperform the state-of-the-art PW DPD based on the indirect learning architecture, as well as the classical single-polynomial based DPD solutions in terms of linearization performance and computational complexity by a clear margin.

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Section: B Novelty and Contributionsmentioning

confidence: 87%

Section: B Novelty and Contributionsmentioning

confidence: 99%

Section: B Gradient-based Pw-decorrelation Dpd Learningmentioning

confidence: 99%

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Piecewise Digital Predistortion fo mmWave Active Antenna Arrays: Algorithms and Measurements

Brihuega,

Abdelaziz,

Anttila

et al. 2020

Preprint

Self Cite

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“…In [40], the authors apply GMP models for the sub-DPD and the thresholds are determined using the slope and the rate of slope of the AM/AM characteristic. In [41], the same technique is used with a learning algorithm that decorrelates the DDR polynomial basis functions and is applied on each sub-DPD independently.…”

Section: Piecewise Modeling In Volterra Based Modelsmentioning

confidence: 99%

“…Authors of [41] apply this approach with a set of GMP models and a learning algorithm that decorrelates the GMP polynomial basis functions. This training is applied on each DPD model independently using the input samples of the corresponding region.…”

Section: Switched Dpdmentioning

confidence: 99%

On Segmented Predistortion for Linearization of RF Power Amplifiers

Baudoin¹

2020

RADIOENGINEERING

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This paper presents a general survey of digital predistortion (DPD) techniques with segmentation. A comparison of global DPD with two segmented approaches namely Vector-Switched DPD and Decomposed Vector Rotation DPD is presented with the support of experimentation on a strongly non-linear 3 ways Doherty PA. It shows the interest of both segmented approaches in terms of linearization performance, complexity and ease of implementation compared to the global DPD. The paper starts with some mathematical generalities on interpolation and splines. It focuses on segmented models derived from Volterra series even if the presented principles can also be applied to neural networks.

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Digital Predistortion in MIMO-OFDM Systems Using Power-Injection Technique

Yang

et al. 2022

2022 International Applied Computational Electromagnetics Society Symposium (ACES-China)

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Decorrelation-based Piecewise Digital Predistortion: Operating Principle and RF Measurements

Cited by 6 publications

References 16 publications

Piecewise Digital Predistortion fo mmWave Active Antenna Arrays: Algorithms and Measurements

Piecewise Digital Predistortion fo mmWave Active Antenna Arrays: Algorithms and Measurements

On Segmented Predistortion for Linearization of RF Power Amplifiers

Digital Predistortion in MIMO-OFDM Systems Using Power-Injection Technique

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