Nonlinear behavioral modeling of power amplifiers using radial-basis function neural networks
M Isaksson, D Wisell, D Ronnow - IEEE MTT-S International …, 2005 - ieeexplore.ieee.org
M Isaksson, D Wisell, D Ronnow
IEEE MTT-S International Microwave Symposium Digest, 2005., 2005•ieeexplore.ieee.orgA 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 …
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 …
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.
ieeexplore.ieee.org
以上显示的是最相近的搜索结果。 查看全部搜索结果