Detail publikace

Digital Predistorter with Real-Valued Feedback Employing Forward Model Estimation

Originální název

Digital Predistorter with Real-Valued Feedback Employing Forward Model Estimation

Anglický název

Digital Predistorter with Real-Valued Feedback Employing Forward Model Estimation

Jazyk

en

Originální abstrakt

Digital predistorters (DPD) are used in modern communication systems to linearise nonlinear power amplifiers (PA) and maximise power efficiency. For their function, a feedback signal from the PA output is required. A conventional DPD uses a quadrature mixer and two analogue-to-digital converters (ADC) which consume additional power and increase system complexity. In this paper we have proposed an innovative technique which allows to use a nonquadrature RF mixer with one ADC in the feedback path. The DPD adaptation is noniterative and based on favoured indirect learning architecture. Firstly, the forward PA model is estimated and subsequently it is used to train DPD coefficients. We have verified and compared the proposed method with other DPD architectures in simulations. The results show that the proposed architecture can achieve the same results as a DPD with complex feedback samples and the other real-valued feedback architectures.

Anglický abstrakt

Digital predistorters (DPD) are used in modern communication systems to linearise nonlinear power amplifiers (PA) and maximise power efficiency. For their function, a feedback signal from the PA output is required. A conventional DPD uses a quadrature mixer and two analogue-to-digital converters (ADC) which consume additional power and increase system complexity. In this paper we have proposed an innovative technique which allows to use a nonquadrature RF mixer with one ADC in the feedback path. The DPD adaptation is noniterative and based on favoured indirect learning architecture. Firstly, the forward PA model is estimated and subsequently it is used to train DPD coefficients. We have verified and compared the proposed method with other DPD architectures in simulations. The results show that the proposed architecture can achieve the same results as a DPD with complex feedback samples and the other real-valued feedback architectures.

BibTex


@inproceedings{BUT149679,
  author="Jan {Král} and Tomáš {Götthans} and Roman {Maršálek} and Michal {Harvánek}",
  title="Digital Predistorter with Real-Valued Feedback Employing Forward Model Estimation",
  annote="Digital predistorters (DPD) are used in modern communication systems to linearise nonlinear power amplifiers (PA) and  maximise  power  efficiency.  For  their  function,  a  feedback signal from the PA output is required. A conventional DPD uses a quadrature mixer and two analogue-to-digital converters (ADC) which consume additional power and increase system complexity. In  this  paper  we  have  proposed  an  innovative  technique  which allows  to  use  a  nonquadrature  RF  mixer  with  one  ADC  in  the feedback  path.  The  DPD  adaptation  is  noniterative  and  based on  favoured  indirect  learning  architecture.  Firstly,  the  forward PA model is estimated and subsequently it is used to train DPD coefficients. We have verified and compared the proposed method with  other  DPD  architectures  in  simulations.  The  results  show that the proposed architecture can achieve the same results as a DPD  with  complex  feedback  samples  and  the  other  real-valued feedback  architectures.",
  booktitle="Proceedings of International Conference on Telecommunications (ICT 2018)",
  chapter="149679",
  doi="10.1109/ICT.2018.8464937",
  howpublished="electronic, physical medium",
  year="2018",
  month="june",
  pages="1--5",
  type="conference paper"
}