Research Article
Evolutionary Multiobjective Optimization for Digital Predistortion Architectures
@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
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.
