Nonlinear behavioral modeling of power amplifiers using radial-basis function neural networks

A radial-basis function neural network (RBFNN) is proposed for modeling the dynamic nonlinear behavior of RF power amplifiers. In the model the signal's envelope is used. The model requires less training than a model using both IQ-data. Sampled input and output signals from a power amplifier fo...

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Bibliographic Details
Main Authors: Isaksson, M., Wisell, D., Ronnow, D.
Format: Conference Proceeding
Language:English
Subjects:
Curve fitting
Electrical engineering, electronics and photonics
Electronics
Elektronik
Elektroteknik, elektronik och fotonik
Modeling
Neural networks
Nonlinear distortion
Passband
Power amplifiers
Radio frequency
radio transmitter
Radio transmitters
Radiofrequency amplifiers
Radiofrequency identification
Signal processing
TECHNOLOGY
TEKNIKVETENSKAP
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Description
Summary:A radial-basis function neural network (RBFNN) is proposed for modeling the dynamic nonlinear behavior of RF power amplifiers. In the model the signal's envelope is used. The model requires less training than a model using both IQ-data. Sampled input and output signals from a power amplifier for 3G were used in the identification and validation. The RBFNN is compared with a parallel Hammerstein model. For a memory depth of one sample the RBFNN gives a better model, in- and out-of-band; for three samples the RBFNN reduces the in-band error more while the Hammerstein model reduces the error out-of-band more.
ISSN:0149-645X
2576-7216
DOI:10.1109/MWSYM.2005.1517128