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.
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