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Low complexity output generalized memory polynomial model for digital predistortion of RF power amplifiers
Author(s) -
Yu Cuiping,
Wang Guangjiang,
Liu Yuan'an
Publication year - 2018
Publication title -
international journal of rf and microwave computer‐aided engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 39
eISSN - 1099-047X
pISSN - 1096-4290
DOI - 10.1002/mmce.21465
Subject(s) - predistortion , amplifier , signal (programming language) , polynomial , computer science , power (physics) , algorithm , mathematics , telecommunications , bandwidth (computing) , mathematical analysis , physics , quantum mechanics , programming language
A novel output generalized memory polynomial (OGMP) behavioral model was proposed in this article, which is based on the previous output signal for digital predistortion (DPD) of power amplifiers (PA). Traditional MP or GMP model use polynomials of the previous input signal to characterize memory effect. Although the OGMP model use polynomials of the previous output signal to characterize memory effect. Using the previous output signal to characterize polynomials of the previous input signal, the number of coefficients will decrease. Measurement results show that the proposed OGMP model can achieve the similar effect with less coefficients. In detail, the complexity of OGMP model reduced by about 50% comparing with MP model. Compared with GMP model, the complexity of OGMP model reduced by about 60% with the similar effect.