A Simplified Sparse Parameter Identification Algorithm Suitable for Power Amplifier Behavioral Modeling

In this letter, a simplified sparse parameter identification algorithm is proposed to estimate the coefficients of the power amplifier (PA) behavioral model. The main idea is to select the kernel one by one from the complete model, where the criteria of selection are according to the projection of the residual vector on each kernel. By using this kernel selection method, all sparse parameters can be obtained quickly without any matrix inversion operation. The proposed algorithm is applied to prune dynamic deviation reduction-based Volterra model for a gallium-nitride (GaN) PA. Moreover, the performance and computation complexity of the proposed method are analyzed and compared with existing algorithms.