A Reduced-Complexity Doubly Orthogonal Matching Pursuit Algorithm for Power Amplifier Sparse Behavioral Modeling

This work presents a simplified version of the doubly orthogonal matching pursuit (DOMP) algorithm to estimate the coefficients of a power amplifier (PA) Volterra behavioral model. The kernels are sequentially selected from the complete model, which requires the pseudoinversion of the measurement matrix after adding a new column. The proposed enhancement avoids the matrix inversions, reducing the computation complexity.