Complexity Optimized Digital Predistortion Model of RF Power Amplifiers
Journal article, 2021

A novel behavioral modeling technique for digital predistortion of radio frequency power amplifiers (PAs) is proposed. The proposed basis-propagating selection (BAPS) model has an advantage on complexity since its basis functions are constructed as delay or a combination of existing basis functions. The BAPS model effectively optimizes the running time complexity while maintaining the generality of a full Volterra series. A greedy search procedure is applied to select the optimal basis functions. Compared with truncated Volterra models and pruned models obtained by popular pruning techniques, the superior performance of the BAPS model, in terms of accuracy and identification stability, has been fairly justified. The linearization measurements on different types of PAs illustrate that the BAPS model can robustly outperform other models from literature while achieving a low running complexity.

Radio frequency

power amplifiers (PA).

Basis-propagating selection (BAPS)

Costs

Complexity theory

Predistortion

modeling and/or linearization techniques

Delays

digital predistortion (DPD)

low-cost complexity

Adaptation models

greedy algorithm

Predictive models

Author

Wenhui Cao

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

Siqi Wang

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

Per Landin

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

Christian Fager

Chalmers, Microtechnology and Nanoscience (MC2), Microwave Electronics

Thomas Eriksson

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks, Communication Systems

IEEE Transactions on Microwave Theory and Techniques

0018-9480 (ISSN)

Vol. In Press

Subject Categories

Bioinformatics (Computational Biology)

Control Engineering

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/TMTT.2021.3122956

More information

Latest update

11/17/2021