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Evolutionary multiobjective optimization for adaptive dataflow-based digital predistortion architectures

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Evolutionary multiobjective optimization for adaptive dataflow-based digital predistortion architectures. / Li, Lin; Ghazi, Amanullah; Boutellier, Jani; Anttila, Lauri; Valkama, Mikko; Bhattacharyya, Shuvra S.

julkaisussa: EAI Endorsed Transactions on Cognitive Communications, Vuosikerta 17, Nro 10, e3, 23.02.2017.

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Harvard

Li, L, Ghazi, A, Boutellier, J, Anttila, L, Valkama, M & Bhattacharyya, SS 2017, 'Evolutionary multiobjective optimization for adaptive dataflow-based digital predistortion architectures', EAI Endorsed Transactions on Cognitive Communications, Vuosikerta. 17, Nro 10, e3. https://doi.org/10.4108/eai.23-2-2017.152187

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Author

Li, Lin ; Ghazi, Amanullah ; Boutellier, Jani ; Anttila, Lauri ; Valkama, Mikko ; Bhattacharyya, Shuvra S. / Evolutionary multiobjective optimization for adaptive dataflow-based digital predistortion architectures. Julkaisussa: EAI Endorsed Transactions on Cognitive Communications. 2017 ; Vuosikerta 17, Nro 10.

Bibtex - Lataa

@article{a8692f63a204427db5459c787af5f48d,
title = "Evolutionary multiobjective optimization for adaptive dataflow-based digital predistortion architectures",
abstract = "In wireless communication systems, high-power transmitters suffer from nonlinearities due to power amplifier (PA) characteristics, I/Q imbalance, and local oscillator (LO) leakage. Digital Predistortion (DPD) is an effective technique to counteract these impairments. To help maximize agility in cognitive radio systems, it is important to investigate dynamically reconfigurable DPD systems that are adaptive to changes in the employed modulation schemes and operational constraints. To help maximize effectiveness, such reconfiguration should be performed based on multidimensional operational criteria. With this motivation, we develop in this paper a novel evolutionary algorithm framework for multiobjective optimization of DPD systems. We demonstrate our framework by applying it to develop an adaptive DPD architecture, called the adaptive, dataflow-based DPD architecture (ADDA), where Pareto-optimized DPD parameters are derived subject to multidimensional constraints to support efficient predistortion across time-varying operational requirements and modulation schemes. Through extensive simulation results, we demonstrate the effectiveness of our proposed multiobjective optimization framework in deriving efficient DPD configurations for run-time adaptation.",
author = "Lin Li and Amanullah Ghazi and Jani Boutellier and Lauri Anttila and Mikko Valkama and Bhattacharyya, {Shuvra S.}",
year = "2017",
month = "2",
day = "23",
doi = "10.4108/eai.23-2-2017.152187",
language = "English",
volume = "17",
journal = "EAI Endorsed Transactions on Cognitive Communications",
issn = "2313-4534",
number = "10",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Evolutionary multiobjective optimization for adaptive dataflow-based digital predistortion architectures

AU - Li, Lin

AU - Ghazi, Amanullah

AU - Boutellier, Jani

AU - Anttila, Lauri

AU - Valkama, Mikko

AU - Bhattacharyya, Shuvra S.

PY - 2017/2/23

Y1 - 2017/2/23

N2 - In wireless communication systems, high-power transmitters suffer from nonlinearities due to power amplifier (PA) characteristics, I/Q imbalance, and local oscillator (LO) leakage. Digital Predistortion (DPD) is an effective technique to counteract these impairments. To help maximize agility in cognitive radio systems, it is important to investigate dynamically reconfigurable DPD systems that are adaptive to changes in the employed modulation schemes and operational constraints. To help maximize effectiveness, such reconfiguration should be performed based on multidimensional operational criteria. With this motivation, we develop in this paper a novel evolutionary algorithm framework for multiobjective optimization of DPD systems. We demonstrate our framework by applying it to develop an adaptive DPD architecture, called the adaptive, dataflow-based DPD architecture (ADDA), where Pareto-optimized DPD parameters are derived subject to multidimensional constraints to support efficient predistortion across time-varying operational requirements and modulation schemes. Through extensive simulation results, we demonstrate the effectiveness of our proposed multiobjective optimization framework in deriving efficient DPD configurations for run-time adaptation.

AB - In wireless communication systems, high-power transmitters suffer from nonlinearities due to power amplifier (PA) characteristics, I/Q imbalance, and local oscillator (LO) leakage. Digital Predistortion (DPD) is an effective technique to counteract these impairments. To help maximize agility in cognitive radio systems, it is important to investigate dynamically reconfigurable DPD systems that are adaptive to changes in the employed modulation schemes and operational constraints. To help maximize effectiveness, such reconfiguration should be performed based on multidimensional operational criteria. With this motivation, we develop in this paper a novel evolutionary algorithm framework for multiobjective optimization of DPD systems. We demonstrate our framework by applying it to develop an adaptive DPD architecture, called the adaptive, dataflow-based DPD architecture (ADDA), where Pareto-optimized DPD parameters are derived subject to multidimensional constraints to support efficient predistortion across time-varying operational requirements and modulation schemes. Through extensive simulation results, we demonstrate the effectiveness of our proposed multiobjective optimization framework in deriving efficient DPD configurations for run-time adaptation.

U2 - 10.4108/eai.23-2-2017.152187

DO - 10.4108/eai.23-2-2017.152187

M3 - Article

VL - 17

JO - EAI Endorsed Transactions on Cognitive Communications

JF - EAI Endorsed Transactions on Cognitive Communications

SN - 2313-4534

IS - 10

M1 - e3

ER -