Generalized Ridge Regression-Based Few-Sample Learning Digital Predistortion

Abstract

Learning from fewer samples can effectively reduce the computational complexity of the parameter identification in digital predistortion (DPD). We refer to this kind of approach as few-sample learning (FSL). However, FSL is always challenging since the ill-conditioning of the matrix will lead to overfitting. In this letter, we explore a stable parameter identification method for FSL DPD based on generalized ridge regression (GRR) and give two closed-form expressions of GRR for fast implementation. Experiments confirm that the proposed method can achieve better performance than the previous methods without any prior knowledge.