基于正交模型辨识辅助的欠采样数字预失真方法

针对数字预失真技术在宽带通信系统中的具体实现受到模数转换器采样率制约的问题，提出一种新的欠采样数字预失真方法。首先在欠采样速率下对功放的正交模型进行离线辨识，接着根据一定的算法得到Nyquist采样率下的简化预失真器模型，并进行在线的自适应直接学习。该方法具有预失真器模型构造简单、欠采样实现高效的特点。理论分析和仿真结果表明,该方法有效降低了对模数转换器的采样率要求，优化了简化预失真器模型的线性化条件，更好地抑制了功放非线性引起的带内失真和频谱再生。

Under-sampling digital predistortion method based on auxiliary orthogonal model identification

In broad-band communication systems, the practical implementation of digital predistortion suffers from the sampling-rate limitation of analog-to-digital converters. In order to solve this problem, an under-sampling digital predistortion method is proposed. First, the power amplifier’s orthogonal model parameters are extracted by offline identification with under-sampling rate. Second, the simplified predistorter with Nyquist rate is obtained according to a certain algorithm. Finally, an adaptive direct-learning algorithm is operated online. The proposed method has the characteristics of simple predistorter model structure and efficient under-sampling realization. Theoretical analysis and simulation results demonstrate that the method reduces the sampling-rate requirement of the analog-to-digital converter effectively and optimizes the linearization condition of the simplified predistorter model, thus it can greatly suppress the in-band distortion and spectrum regrowth caused by the nonlinearities of the power amplifier.