Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks

A radial basis function (RBF) artificial neural network model for a designed high efficiency radio frequency class-F power amplifier (PA) is presented in this paper. The presented amplifier is designed at 1.8 GHz operating frequency with 12 dB of gain and 36 dBm of 1dB output compression point. The obtained power added efficiency (PAE) for the presented PA is 76% under 26 dBm input power. The proposed RBF model uses input and DC power of the PA as inputs variables and considers output power as the output variable. The presented RBF network models the designed class-F PA as a block, which could be applied in circuit design. The presented model could be used to model any RF power amplifier. The obtained results show a good agreement between real data and predicted values by RBF model. The results clearly show that the presented RBF network is more precise than multilayer perceptron (MLP) model. According to the results, better than 84% and 92% improvement is achieved in MAE and RMSE, respectively

@article{Zirak2016,
  title     = {Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {6},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Ali Reza Zirak and Sobhan Roshani},
  doi       = {10.14569/IJACSA.2016.070629},
  url       = {https://doi.org/10.14569/IJACSA.2016.070629}
}