In this article, an improved nonlinear smooth twin support vector regression (NSTSVR) model is proposed for the modeling and compensating of the transmitter nonlinearities jointly. The proposed model is an improved version of the twin support vector regression (TSVR) model by introducing a smooth function to replace the loss function of TSVR, which can change the dual space solution to the original space solution and speed up the solving solution. In addition, in order to solve the problem of long training time for large sample data in traditional SVR or TSVR model, the new algorithm further adopts the model pruning techniques, such as deleting the kernel matrix and finding sparse diagonal matrices, to reduce the size of the Hessian matrix in the fast Newton iteration process. To verify the performance of the proposed model, two transmitters based on single‐device gallium nitride (GaN) PA with IQ imbalance and GaN Doherty PA with modulator imperfections are used for experimental verification and analysis. The experimental results show that the proposed model is superior to the conventional support vector regression and TSVR machine learning models in terms of modeling effect and linearization ability. Furthermore, the proposed model can achieve the improved compensation performance for transmitter impairments compared with some popular Volterra series‐based I/Q imbalance models.

中文翻译：

---

基于频率的发射机非线性联合补偿的改进非线性光滑双支撑向量回归行为模型

本文提出了一种改进的非线性光滑双支撑向量回归（NSTSVR）模型，用于联合建模和补偿发射机的非线性。提出的模型是对孪生支持向量回归（TSVR）模型的改进版本，它引入了平滑函数来代替TSVR的损失函数，从而可以将对偶空间解更改为原始空间解并加快求解速度。另外，为了解决传统的SVR或TSVR模型中大样本数据训练时间长的问题，新算法还采用了模型修剪技术，如删除核矩阵，找到稀疏对角矩阵，以减小大小。牛顿快速迭代过程中的Hessian矩阵的求和。为了验证所提出模型的性能，两个基于IQ失衡的单器件氮化镓（GaN）PA和具有调制器缺陷的GaN Doherty PA的发射器用于实验验证和分析。实验结果表明，该模型在建模效果和线性化能力上均优于传统的支持向量回归和TSVR机器学习模型。此外，与一些流行的基于Volterra系列的基于I / Q的不平衡模型相比，该模型可以改善发射机的补偿性能。实验结果表明，该模型在建模效果和线性化能力上均优于传统的支持向量回归和TSVR机器学习模型。此外，与一些流行的基于Volterra系列的基于I / Q的不平衡模型相比，该模型可以改善发射机的补偿性能。实验结果表明，该模型在建模效果和线性化能力上均优于传统的支持向量回归和TSVR机器学习模型。此外，与一些流行的基于Volterra系列的基于I / Q的不平衡模型相比，该模型可以改善发射机的补偿性能。