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.