Open AccessDynamic deviation memory polynomial model for digital predistortion
Author(s) -
Song Bin,
He Songbai,
Peng Jun,
Zhao Yatao
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2017.0226
Subject(s) - predistortion , amplifier , polynomial , reduction (mathematics) , polynomial and rational function modeling , computer science , volterra series , distortion (music) , dynamic random access memory , power (physics) , algorithm , control theory (sociology) , electronic engineering , mathematics , nonlinear system , semiconductor memory , artificial intelligence , engineering , bandwidth (computing) , telecommunications , computer hardware , mathematical analysis , physics , geometry , control (management) , quantum mechanics
A dynamic deviation memory polynomial (DDMP) model for digital predistortion is proposed to compensate the non‐linear distortion of the power amplifier. It is based on the memory polynomial model and dynamic deviation reduction‐based Volterra (DDRV) model. Compared to the DDRV model, the proposed model shows better modelling accuracy under the same complexity. The effectiveness of the DDMP model has been verified by simulation and experiment results.