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Neural networks and volterra series for time‐domain power amplifier behavioral models
Author(s) -
Giannini F.,
Colantonio P.,
Orengo G.,
Serino A.,
Stegmayer G.,
Pirola M.,
Ghione G.
Publication year - 2007
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.20210
Subject(s) - volterra series , amplifier , artificial neural network , time domain , computer science , bandwidth (computing) , nonlinear system , behavioral modeling , frequency domain , feedforward neural network , electronic engineering , control theory (sociology) , speech recognition , engineering , telecommunications , artificial intelligence , physics , control (management) , quantum mechanics , computer vision
This paper presents a black‐box model that can be applied to characterize the nonlinear dynamic behavior of power amplifiers (PAs), including strong nonlinearities and memory effects. Feedforward time‐delay Neural Networks (TDNN) are used to extract the model from a large‐signal input‐output time‐domain characterization in a given bandwidth; furthermore, explicit formulas to derive Volterra kernels from the TDNN parameters are also presented. The TDNN and related Volterra models can predict the amplifier response to different frequency excitations in the same bandwidth and power sweep. As a case study, a PA, characterized with a two‐tone power swept excitation, is modeled and simulations are found in good agreement with training measurements; moreover, a model validation with two tones of different frequencies and spacing is also performed. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.