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.

中文翻译：

---

用于计算高效 PA 行为建模和 DPD 线性化的网格选择

这封信提出了一种用于复值信号的网格选择 (MeS) 方法，旨在显着减少提取数学模型参数所需的训练数据，以表征功率放大器或数字预失真线性化器的非线性行为。当与降维技术适当结合时，实验结果将显示所提出的 MeS 方法的优势。就使用连续样本进行训练和采用常用的 QR 最小二乘法解决方案而言，可以将参数的识别计算复杂度降低 65 倍。