一种用于宽带功率放大器的低计算复杂度数字预失真模型。

Lu Xu, Zhou Qiang, Zhu Lei, Wei Zhihu, Wu Yaqi, Liu Zunyan, Chen Zhang

School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210007, China.

The Sixty-Third Research Institute, National University of Defense Technology, Nanjing 210007, China.

Sensors (Basel). 2024 Oct 29;24(21):6941. doi: 10.3390/s24216941.

This paper proposes a Composition Piecewise Memory Polynomial (CPMP) digital predistortion model based on a Vector Switched (VS) behavioral model to address the challenges of severe nonlinearity and strong memory effects in wideband power amplifiers (PAs). To tackle this issue, two thresholds are calculated and used to segment the envelope values of the input signal according to the nonlinear distortion characteristics of the PA. In this approach, a Generalized Memory Polynomial (GMP) model is employed for the lower segment, a Memory Polynomial (MP) model is employed for the middle segment, and a higher-order GMP model is employed for the upper segment. By sharing the fundamental MP among the proposed segmented models and leveraging a design methodology that configures different cross terms, memory depths, and polynomial orders for each segment, this model achieves superior linearization performance while simultaneously reducing the computational complexity associated with model extraction. The experimental results demonstrate that the adjacent channel power ratio (ACPR) of the predistorted PA output signal using the proposed model improves from -36 dBc to -54 dBc, matching the performance of the GMP model. Furthermore, this performance is 0.5 dBc better than the Piecewise Dynamic Deviation Reduction (PDDR) and Decomposed Vector Rotation (DVR) models. Notably, the complexity of the proposed parameter extraction process is 28.8% of the DVR model, 21.79% of the GMP model, and 12.83% of the PDDR model.

本文提出了一种基于矢量切换（VS）行为模型的复合分段记忆多项式（CPMP）数字预失真模型，以应对宽带功率放大器（PA）中严重非线性和强记忆效应的挑战。为了解决这个问题，根据PA的非线性失真特性计算两个阈值，并用于对输入信号的包络值进行分段。在这种方法中，下段采用广义记忆多项式（GMP）模型，中段采用记忆多项式（MP）模型，上段采用高阶GMP模型。通过在所提出的分段模型之间共享基本MP，并利用一种为每个段配置不同交叉项、记忆深度和多项式阶数的设计方法，该模型在实现卓越线性化性能的同时，还降低了与模型提取相关的计算复杂度。实验结果表明，使用所提出模型的预失真PA输出信号的邻道功率比（ACPR）从-36 dBc提高到-54 dBc，与GMP模型的性能相当。此外，该性能比分段动态偏差降低（PDDR）和分解矢量旋转（DVR）模型好0.5 dBc。值得注意的是，所提出的参数提取过程的复杂度分别为DVR模型的28.8%、GMP模型的21.79%和PDDR模型的12.83%。