Under-sampling effects and computational cost reduction in RF power amplifier behavioral modeling

This paper presents a study oriented at reducing the computational complexity of least squares (LS) identification of the parameters describing power amplifier´s (PA) behavioral models. To reduce the dimensions of the input data matrix, two strategies are proposed: i) model order reduction based on the principal component analysis (PCA) theory; and ii) apply a mesh-selecting method to reduce the number of required equations. In this context, the effect of using under-sampling ADCs for the LS parameter extraction aiming at reducing the costs of PA identification is also discussed. Finally, the trade-off between the cost/complexity reduction and quality (or identification accuracy) loss is evaluated. The proposed strategies can also be considered for low-computational cost digital predistortion implementations.