利用随机森林确定宽带功率放大器数字预失真器模型结构

2020 IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications (PAWR) Pub Date : 2020-01-01 DOI:10.1109/PAWR46754.2020.9036004

Luis Álvarez-López, Juan A. Becerra, M. J. Madero-Ayora, C. Crespo-Cadenas

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引用次数: 5

摘要

本文介绍了随机森林技术在行为建模组件选择中的应用。为了得到各回归量对功率放大器线性化误差影响的重要程度序列，采用了几种随机结构的数字预失真器(DPDs)。该方法已在5G-NR信号下运行的商用PA的DPD中得到验证，展示了该算法对组件进行排序和修剪的能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Determining a Digital Predistorter Model Structure for Wideband Power Amplifiers through Random Forest

This work presents the application of the Random Forest technique to behavioral modeling component selection. Several digital predistorters (DPDs) with a random structure are attained to obtain a sequence of the regressors importance with respect to their impact in the linearization error of a power amplifier (PA). The approach has been validated in the DPD of a commercial PA operating under a 5G-NR signal, showcasing the ability of the algorithm to sort and prune the components.

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2020 IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications (PAWR)

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