Cognitive Radio Oriented Wireless Networks. 11th International Conference, CROWNCOM 2016, Grenoble, France, May 30 - June 1, 2016, Proceedings

Research Article

Evolutionary Multiobjective Optimization for Digital Predistortion Architectures

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  • @INPROCEEDINGS{10.1007/978-3-319-40352-6_41,
        author={Lin Li and Amanullah Ghazi and Jani Boutellier and Lauri Anttila and Mikko Valkama and Shuvra Bhattacharyya},
        title={Evolutionary Multiobjective Optimization for Digital Predistortion Architectures},
        proceedings={Cognitive Radio Oriented Wireless Networks. 11th International Conference, CROWNCOM 2016, Grenoble, France, May 30 - June 1, 2016, Proceedings},
        proceedings_a={CROWNCOM},
        year={2016},
        month={6},
        keywords={Digital predistortion Multiobjective optimization Evolutionary algorithms},
        doi={10.1007/978-3-319-40352-6_41}
    }
    
  • Lin Li
    Amanullah Ghazi
    Jani Boutellier
    Lauri Anttila
    Mikko Valkama
    Shuvra Bhattacharyya
    Year: 2016
    Evolutionary Multiobjective Optimization for Digital Predistortion Architectures
    CROWNCOM
    Springer
    DOI: 10.1007/978-3-319-40352-6_41
Lin Li1,*, Amanullah Ghazi2,*, Jani Boutellier2,*, Lauri Anttila3,*, Mikko Valkama3,*, Shuvra Bhattacharyya,*
  • 1: University of Maryland
  • 2: University of Oulu
  • 3: Tampere University of Technology
*Contact email: lli12311@umd.edu, amanullah.ghazi@ee.oulu.fi, jani.boutellier@ee.oulu.fi, lauri.anttila@tut.fi, mikko.e.valkama@tut.fi, ssb@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. () 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 (), 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.