Evolutionary Multiobjective Optimization for Adaptive Dataflow-based Digital Predistortion Architectures

Lin Li; Amanullah Ghazi; Jani Boutellier; Lauri Anttila; Mikko Valkama; Shuvra S. Bhattacharyya

Research Article

Evolutionary Multiobjective Optimization for Adaptive Dataflow-based Digital Predistortion Architectures

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@ARTICLE{10.4108/eai.23-2-2017.152187,
    author={Lin Li and Amanullah Ghazi and Jani Boutellier and Lauri Anttila and Mikko Valkama and Shuvra S. Bhattacharyya},
    title={Evolutionary Multiobjective Optimization for Adaptive Dataflow-based Digital Predistortion Architectures},
    journal={EAI Endorsed Transactions on Cognitive Communications},
    volume={3},
    number={10},
    publisher={EAI},
    journal_a={COGCOM},
    year={2017},
    month={2},
    keywords={Digital predistortion, multiobjective optimization, evolutionary algorithms},
    doi={10.4108/eai.23-2-2017.152187}
}

Lin Li
Amanullah Ghazi
Jani Boutellier
Lauri Anttila
Mikko Valkama
Shuvra S. Bhattacharyya
Year: 2017
Evolutionary Multiobjective Optimization for Adaptive Dataflow-based Digital Predistortion Architectures
COGCOM
EAI
DOI: 10.4108/eai.23-2-2017.152187

Lin Li¹^,*, Amanullah Ghazi², Jani Boutellier², Lauri Anttila³, Mikko Valkama³, Shuvra S. Bhattacharyya⁴

1: University of Maryland, College Park, ECE Department, College Park, MD 20742, USA
2: University of Oulu, Dept. Computer Science and Engineering, Finland
3: Tampere University of Technology, Dept. Electronics and Communications Engineering, Finland
4: University of Maryland, College Park, ECE Department, College Park, MD 20742, USA; Tampere University of Technology, Dept. Electronics and Communications Engineering, Finland

*Contact email: lli12311@umd.edu

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.

Keywords: Digital predistortion, multiobjective optimization, evolutionary algorithms

Received: 2016-08-20
Accepted: 2016-05-31
Published: 2017-02-23
Publisher: EAI

: http://dx.doi.org/10.4108/eai.23-2-2017.152187

Copyright © 2017 M. Höyhtyä et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.