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Evolutionary Multiobjective Optimization for Digital Predistortion Architectures

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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.

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Acknowledgements

This research was supported in part by Tekes, the Finnish Funding Agency for Innovation; and the U.S. National Science Foundation.

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Authors and Affiliations

  1. ECE Department, University of Maryland, College Park, MD, 20742, USA

    Lin Li & Shuvra S. Bhattacharyya

  2. Department of Computer Science and Engineering, University of Oulu, Oulu, Finland

    Amanullah Ghazi & Jani Boutellier

  3. Department of Electronics and Communications Engineering, Tampere University of Technology, Tampere, Finland

    Lauri Anttila, Mikko Valkama & Shuvra S. Bhattacharyya

Authors
  1. Lin Li
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  2. Amanullah Ghazi
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  3. Jani Boutellier
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  4. Lauri Anttila
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  5. Mikko Valkama
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  6. Shuvra S. Bhattacharyya
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Corresponding author

Correspondence to Lin Li .

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Editors and Affiliations

  1. CEA-LETI , Grenoble, France

    Dominique Noguet

  2. University of Surrey, Guildford, United Kingdom

    Klaus Moessner

  3. CentraleSupélec – Campus de Rennes, Institut d'Électronique et de Télécommunications de Rennes UMR CNRS 6164, Cesson-Sévigné, France

    Jacques Palicot

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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Li, L., Ghazi, A., Boutellier, J., Anttila, L., Valkama, M., Bhattacharyya, S.S. (2016). Evolutionary Multiobjective Optimization for Digital Predistortion Architectures. In: Noguet, D., Moessner, K., Palicot, J. (eds) Cognitive Radio Oriented Wireless Networks. CrownCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 172. Springer, Cham. https://doi.org/10.1007/978-3-319-40352-6_41

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  • DOI: https://doi.org/10.1007/978-3-319-40352-6_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40351-9

  • Online ISBN: 978-3-319-40352-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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