cogcom 17(10): e3

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.