Mesh-Selecting for Computational Efficient PA Behavioral Modeling and DPD Linearization

This letter proposes a mesh-selecting (MeS) method for complex-valued signals oriented at significantly reducing the training data required to extract the parameters of mathematical models for characterizing the nonlinear behavior of power amplifiers or digital predistortion linearizers. Experimental results will show the advantages of the proposed MeS method when properly combined with dimensionality reduction techniques. A reduction of the parameters' identification computational complexity by a factor of 65 can be achieved with respect to training with consecutive samples and employing the commonly used QR least-squares solution.

Automated summary

The proposed MeS method, when combined with dimensionality reduction techniques, can significantly reduce training data and decrease the computational complexity of parameter identification.